import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.neighbors import KNeighborsRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor,AdaBoostRegressor,GradientBoostingRegressor, ExtraTreesRegressor
from sklearn.svm import SVR
from sklearn.linear_model import LinearRegression,Lasso, Ridge
from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error
from catboost import CatBoostRegressor
from xgboost import XGBRegressor
from sklearn.svm import SVR, NuSVR
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.metrics import make_scorer
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LeakyReLU
from warnings import filterwarnings
filterwarnings("ignore")
df = pd.read_csv("/kaggle/input/biomass-cleaned-dataset/biomass data.csv")
df.describe()
| sl no | MC | VM | FC | Ash | C | H | O | N | S | oC | ER | S/B | CO | CO2 | H2 | CH4 | Gas (m3/kg) | Tar (g/m^3) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 450.000000 | 450.000000 | 450.000000 | 450.000000 | 450.000000 | 450.000000 | 450.000000 | 450.000000 | 450.000000 | 450.000000 | 450.000000 | 450.000000 | 450.000000 | 414.000000 | 414.000000 | 414.000000 | 414.000000 | 268.000000 | 124.000000 |
| mean | 225.500000 | 8.527356 | 71.909667 | 15.288511 | 4.289467 | 49.434578 | 6.090178 | 43.443067 | 0.699333 | 0.334000 | 802.198000 | 0.175600 | 0.612667 | 30.953309 | 28.829203 | 32.212874 | 8.004638 | 1.569813 | 13.713065 |
| std | 130.048068 | 3.672753 | 7.987731 | 4.021544 | 5.753450 | 3.342011 | 1.229714 | 3.929314 | 0.935660 | 0.719897 | 87.353518 | 0.140539 | 0.747409 | 9.045725 | 10.765455 | 13.362743 | 3.340170 | 0.657992 | 18.370045 |
| min | 1.000000 | 4.560000 | 52.560000 | 3.120000 | 0.010000 | 43.300000 | 0.080000 | 31.010000 | 0.010000 | 0.000000 | 599.000000 | 0.000000 | 0.000000 | 7.370000 | 5.000000 | 6.360000 | 0.430000 | 0.260000 | 0.540000 |
| 25% | 113.250000 | 6.110000 | 66.900000 | 12.570000 | 0.500000 | 46.920000 | 5.620000 | 41.340000 | 0.160000 | 0.030000 | 750.000000 | 0.000000 | 0.000000 | 23.865000 | 20.072500 | 22.610000 | 5.715000 | 1.100000 | 3.475000 |
| 50% | 225.500000 | 8.000000 | 75.180000 | 15.610000 | 1.510000 | 50.200000 | 6.210000 | 42.990000 | 0.530000 | 0.110000 | 800.000000 | 0.210000 | 0.390000 | 31.475000 | 28.255000 | 30.065000 | 7.930000 | 1.520000 | 7.650000 |
| 75% | 337.750000 | 9.800000 | 77.710000 | 16.940000 | 5.330000 | 50.820000 | 6.780000 | 46.420000 | 0.900000 | 0.400000 | 850.000000 | 0.280000 | 1.050000 | 38.052500 | 36.530000 | 40.875000 | 10.000000 | 2.100000 | 15.125000 |
| max | 450.000000 | 27.000000 | 86.740000 | 26.450000 | 19.520000 | 58.340000 | 8.660000 | 51.830000 | 6.550000 | 4.200000 | 1108.000000 | 0.500000 | 4.700000 | 50.560000 | 59.040000 | 65.660000 | 22.000000 | 3.300000 | 91.430000 |
df.head()
| sl no | Biomass species | MC | VM | FC | Ash | C | H | O | N | S | oC | ER | S/B | CO | CO2 | H2 | CH4 | Gas (m3/kg) | Tar (g/m^3) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.0 | Corn Stover | 6.34 | 67.25 | 15.64 | 10.68 | 52.26 | 6.03 | 40.67 | 0.97 | 0.07 | 650.0 | 0.0 | 1.0 | 27.26 | 26.16 | 29.69 | 16.89 | NaN | NaN |
| 1 | 2.0 | Vermont Wood | 4.56 | 81.51 | 13.55 | 0.38 | 54.51 | 6.21 | 39.15 | 0.11 | 0.03 | 650.0 | 0.0 | 1.0 | 25.66 | 26.20 | 31.22 | 16.92 | NaN | NaN |
| 2 | 3.0 | Wheat Straw | 5.18 | 67.89 | 14.89 | 12.04 | 58.34 | 6.40 | 34.79 | 0.36 | 0.11 | 650.0 | 0.0 | 1.0 | 30.15 | 24.12 | 27.85 | 17.87 | NaN | NaN |
| 3 | 4.0 | Switchgrass | 8.38 | 69.63 | 14.66 | 7.33 | 50.61 | 5.82 | 42.77 | 0.71 | 0.10 | 650.0 | 0.0 | 1.0 | 35.66 | 20.84 | 25.24 | 18.26 | NaN | NaN |
| 4 | 5.0 | Rice Husk | 9.84 | 65.07 | 16.13 | 8.96 | 45.09 | 5.93 | 46.87 | 0.59 | 1.52 | 850.0 | 0.0 | 0.3 | 37.28 | 15.11 | 37.78 | 9.82 | 0.4 | NaN |
df.columns
Index(['sl no', 'Biomass species', 'MC', 'VM', 'FC', 'Ash', 'C', 'H', 'O', 'N',
'S', 'oC', 'ER', 'S/B', 'CO', 'CO2', 'H2', 'CH4', 'Gas (m3/kg)',
'Tar (g/m^3)'],
dtype='object')
df.drop(columns = ['sl no'], inplace = True)
df.columns
Index(['Biomass species', 'MC', 'VM', 'FC', 'Ash', 'C', 'H', 'O', 'N', 'S',
'oC', 'ER', 'S/B', 'CO', 'CO2', 'H2', 'CH4', 'Gas (m3/kg)',
'Tar (g/m^3)'],
dtype='object')
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 453 entries, 0 to 452 Data columns (total 19 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Biomass species 450 non-null object 1 MC 450 non-null float64 2 VM 450 non-null float64 3 FC 450 non-null float64 4 Ash 450 non-null float64 5 C 450 non-null float64 6 H 450 non-null float64 7 O 450 non-null float64 8 N 450 non-null float64 9 S 450 non-null float64 10 oC 450 non-null float64 11 ER 450 non-null float64 12 S/B 450 non-null float64 13 CO 414 non-null float64 14 CO2 414 non-null float64 15 H2 414 non-null float64 16 CH4 414 non-null float64 17 Gas (m3/kg) 268 non-null float64 18 Tar (g/m^3) 124 non-null float64 dtypes: float64(18), object(1) memory usage: 67.4+ KB
df['Biomass species'].value_counts()
Biomass species Pine Sawdust 70 Rice Husk 32 Wood Residue 25 Sawdust 23 Pine wood 19 Empty Fruit Bunch 19 Wood Pellets 17 Rice husk 17 Rice Straw 15 Pine Chips 15 Artificial waste (including wood chips) 14 Groundnut Shell 13 Sugarcane Bagasse 13 Corn Straw 12 Ecualyptus Sawdust 10 Peat 9 Palm Oil Wastes 9 Legume Straw 9 Wood Chips 9 Coconut Shell 8 Rubber Woodchip 7 Pine waste 7 Olive Stone 7 C. cardunculus L 5 Olive Tree Cuttings 5 Rubber Wood Chip 5 Miscanthus Pellet 5 Olive kernels 5 Poultry litter 4 Sunflower 4 Orujillo 4 Crushed Peat Pellets 4 Willow 4 Holm-oak 4 Woody biomass 4 Eucalyptus 4 Wood Pellet 3 Spruce Wood Pellets 3 Vermont Wood 1 Corn Stover 1 Switchgrass 1 Wheat Straw 1 Artificial waste(including wood chips) 1 Dried Grains 1 Woody Biomass 1 Bark Pellet 1 Name: count, dtype: int64
# sns.pairplot(df, hue='Biomass species')
# plt.show()
categorical_col = ['Biomass species']
numerical_col = df.select_dtypes(include='float').columns.tolist()
target_col = numerical_col[12:18]
numerical_col = numerical_col[:12]
print(numerical_col)
print(target_col)
print(categorical_col)
['MC', 'VM', 'FC', 'Ash', 'C', 'H', 'O', 'N', 'S', 'oC', 'ER', 'S/B'] ['CO', 'CO2', 'H2', 'CH4', 'Gas (m3/kg)', 'Tar (g/m^3)'] ['Biomass species']
for col in numerical_col:
plt.figure(figsize=(6, 4))
sns.kdeplot(data=df[col], fill=True)
plt.title(f'KDE of {col}')
plt.xlabel(col)
plt.ylabel('Density')
plt.tight_layout()
plt.show()
for col in numerical_col:
plt.figure(figsize=(6, 4))
sns.boxplot(x=df[col])
plt.title(f'Boxplot of {col}')
plt.tight_layout()
plt.show()
There are a few outliers present in the dataset
plt.figure(figsize= (20,20))
corr = df.select_dtypes(include='number').corr()
sns.heatmap(corr, annot=True, cmap='coolwarm')
plt.title('Correlation between Features')
plt.show()
Ash and VM has 0.78 neg correlation and O and C have very high negative correlation
plt.figure(figsize=(80,16))
sns.countplot(x = 'Biomass species', data = df);
There is an uneven distribution in the count of categorical values
for tar_col in target_col:
for num_col in numerical_col:
plt.figure(figsize=(6,4))
sns.scatterplot(x = df[num_col], y = df[tar_col])
plt.show()
df = df.iloc[:-3]
df.tail()
| Biomass species | MC | VM | FC | Ash | C | H | O | N | S | oC | ER | S/B | CO | CO2 | H2 | CH4 | Gas (m3/kg) | Tar (g/m^3) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 445 | Wood Pellets | 5.92 | 76.13 | 17.29 | 0.66 | 49.87 | 5.81 | 42.26 | 2.0 | 0.06 | 813.0 | 0.30 | 0.0 | NaN | NaN | NaN | NaN | 1.02 | 16.9 |
| 446 | Orujillo | 8.00 | 59.83 | 19.12 | 13.05 | 53.32 | 6.10 | 38.31 | 2.1 | 0.17 | 770.0 | 0.16 | 0.0 | NaN | NaN | NaN | NaN | 1.21 | 16.1 |
| 447 | Orujillo | 8.00 | 59.83 | 19.12 | 13.05 | 53.32 | 6.10 | 38.31 | 2.1 | 0.17 | 830.0 | 0.23 | 0.0 | NaN | NaN | NaN | NaN | 1.37 | 12.3 |
| 448 | Orujillo | 8.00 | 59.83 | 19.12 | 13.05 | 53.32 | 6.10 | 38.31 | 2.1 | 0.17 | 835.0 | 0.24 | 0.0 | NaN | NaN | NaN | NaN | 1.41 | 11.6 |
| 449 | Orujillo | 8.00 | 59.83 | 19.12 | 13.05 | 53.32 | 6.10 | 38.31 | 2.1 | 0.17 | 870.0 | 0.31 | 0.0 | NaN | NaN | NaN | NaN | 1.47 | 9.9 |
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df['Biomass species'] = le.fit_transform(df['Biomass species'])
df['Biomass species'].unique().shape
(46,)
df.tail()
| Biomass species | MC | VM | FC | Ash | C | H | O | N | S | oC | ER | S/B | CO | CO2 | H2 | CH4 | Gas (m3/kg) | Tar (g/m^3) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 445 | 42 | 5.92 | 76.13 | 17.29 | 0.66 | 49.87 | 5.81 | 42.26 | 2.0 | 0.06 | 813.0 | 0.30 | 0.0 | NaN | NaN | NaN | NaN | 1.02 | 16.9 |
| 446 | 19 | 8.00 | 59.83 | 19.12 | 13.05 | 53.32 | 6.10 | 38.31 | 2.1 | 0.17 | 770.0 | 0.16 | 0.0 | NaN | NaN | NaN | NaN | 1.21 | 16.1 |
| 447 | 19 | 8.00 | 59.83 | 19.12 | 13.05 | 53.32 | 6.10 | 38.31 | 2.1 | 0.17 | 830.0 | 0.23 | 0.0 | NaN | NaN | NaN | NaN | 1.37 | 12.3 |
| 448 | 19 | 8.00 | 59.83 | 19.12 | 13.05 | 53.32 | 6.10 | 38.31 | 2.1 | 0.17 | 835.0 | 0.24 | 0.0 | NaN | NaN | NaN | NaN | 1.41 | 11.6 |
| 449 | 19 | 8.00 | 59.83 | 19.12 | 13.05 | 53.32 | 6.10 | 38.31 | 2.1 | 0.17 | 870.0 | 0.31 | 0.0 | NaN | NaN | NaN | NaN | 1.47 | 9.9 |
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 450 entries, 0 to 449 Data columns (total 19 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Biomass species 450 non-null int64 1 MC 450 non-null float64 2 VM 450 non-null float64 3 FC 450 non-null float64 4 Ash 450 non-null float64 5 C 450 non-null float64 6 H 450 non-null float64 7 O 450 non-null float64 8 N 450 non-null float64 9 S 450 non-null float64 10 oC 450 non-null float64 11 ER 450 non-null float64 12 S/B 450 non-null float64 13 CO 414 non-null float64 14 CO2 414 non-null float64 15 H2 414 non-null float64 16 CH4 414 non-null float64 17 Gas (m3/kg) 268 non-null float64 18 Tar (g/m^3) 124 non-null float64 dtypes: float64(18), int64(1) memory usage: 66.9 KB
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
data = df[['Ash', 'VM']]
# Scale the data
scaler = StandardScaler()
data_scaled = scaler.fit_transform(data)
# Step 2: Perform PCA
# Since we have two features, we can set n_components to 2 (or less, if you want to reduce dimensions further)
pca = PCA(n_components=2)
principal_components = pca.fit_transform(data_scaled)
# Step 3: Create a DataFrame with the principal components
pc_df = pd.DataFrame(data=principal_components, columns=['PC1', 'PC2'])
print("Principal Components:")
print(pc_df)
print("\nExplained Variance Ratio:")
print(pca.explained_variance_ratio_)
Principal Components:
PC1 PC2
0 1.199231 -0.373327
1 -1.331820 -0.369793
2 1.309845 -0.597378
3 0.576132 -0.172071
4 1.180803 0.031496
.. ... ...
445 -0.820580 0.072546
446 2.148411 -0.007348
447 2.148411 -0.007348
448 2.148411 -0.007348
449 2.148411 -0.007348
[450 rows x 2 columns]
Explained Variance Ratio:
[0.88808869 0.11191131]
Between Ash and VM the PC1 has captured 88% variance as it is not more than 90 so I am not replacing them in the original dataframe
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
data = df[['O', 'C']]
# Scale the data
scaler = StandardScaler()
data_scaled = scaler.fit_transform(data)
# Step 2: Perform PCA
# Since we have two features, we can set n_components to 2 (or less, if you want to reduce dimensions further)
pca = PCA(n_components=2)
principal_components = pca.fit_transform(data_scaled)
# Step 3: Create a DataFrame with the principal components
pc_df = pd.DataFrame(data=principal_components, columns=['PC1', 'PC2'])
print("Principal Components:")
print(pc_df)
print("\nExplained Variance Ratio:")
print(pca.explained_variance_ratio_)
Principal Components:
PC1 PC2
0 1.098059 0.098884
1 1.848486 0.301633
2 3.445228 0.327406
3 0.370232 0.127716
4 -1.537640 -0.302868
.. ... ...
445 0.305368 -0.120908
446 1.747756 -0.101761
447 1.747756 -0.101761
448 1.747756 -0.101761
449 1.747756 -0.101761
[450 rows x 2 columns]
Explained Variance Ratio:
[0.95675092 0.04324908]
# Optionally, if you're sure, drop the original 'O' and 'C' columns
df_reduced = df.drop(['O', 'C'], axis=1)
df_reduced['PC1'] = principal_components[:, 0]
replaced the C and O column with PC1 and the variance captured by PC1 is more than 95%
df_reduced.head()
| Biomass species | MC | VM | FC | Ash | H | N | S | oC | ER | S/B | CO | CO2 | H2 | CH4 | Gas (m3/kg) | Tar (g/m^3) | PC1 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 5 | 6.34 | 67.25 | 15.64 | 10.68 | 6.03 | 0.97 | 0.07 | 650.0 | 0.0 | 1.0 | 27.26 | 26.16 | 29.69 | 16.89 | NaN | NaN | 1.098059 |
| 1 | 37 | 4.56 | 81.51 | 13.55 | 0.38 | 6.21 | 0.11 | 0.03 | 650.0 | 0.0 | 1.0 | 25.66 | 26.20 | 31.22 | 16.92 | NaN | NaN | 1.848486 |
| 2 | 38 | 5.18 | 67.89 | 14.89 | 12.04 | 6.40 | 0.36 | 0.11 | 650.0 | 0.0 | 1.0 | 30.15 | 24.12 | 27.85 | 17.87 | NaN | NaN | 3.445228 |
| 3 | 36 | 8.38 | 69.63 | 14.66 | 7.33 | 5.82 | 0.71 | 0.10 | 650.0 | 0.0 | 1.0 | 35.66 | 20.84 | 25.24 | 18.26 | NaN | NaN | 0.370232 |
| 4 | 27 | 9.84 | 65.07 | 16.13 | 8.96 | 5.93 | 0.59 | 1.52 | 850.0 | 0.0 | 0.3 | 37.28 | 15.11 | 37.78 | 9.82 | 0.4 | NaN | -1.537640 |
df_reduced.describe()
| Biomass species | MC | VM | FC | Ash | H | N | S | oC | ER | S/B | CO | CO2 | H2 | CH4 | Gas (m3/kg) | Tar (g/m^3) | PC1 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 450.000000 | 450.000000 | 450.000000 | 450.000000 | 450.000000 | 450.000000 | 450.000000 | 450.000000 | 450.000000 | 450.000000 | 450.000000 | 414.000000 | 414.000000 | 414.000000 | 414.000000 | 268.000000 | 124.000000 | 4.500000e+02 |
| mean | 23.642222 | 8.527356 | 71.909667 | 15.288511 | 4.289467 | 6.090178 | 0.699333 | 0.334000 | 802.198000 | 0.175600 | 0.612667 | 30.953309 | 28.829203 | 32.212874 | 8.004638 | 1.569813 | 13.713065 | -3.157968e-17 |
| std | 11.366676 | 3.672753 | 7.987731 | 4.021544 | 5.753450 | 1.229714 | 0.935660 | 0.719897 | 87.353518 | 0.140539 | 0.747409 | 9.045725 | 10.765455 | 13.362743 | 3.340170 | 0.657992 | 18.370045 | 1.384833e+00 |
| min | 0.000000 | 4.560000 | 52.560000 | 3.120000 | 0.010000 | 0.080000 | 0.010000 | 0.000000 | 599.000000 | 0.000000 | 0.000000 | 7.370000 | 5.000000 | 6.360000 | 0.430000 | 0.260000 | 0.540000 | -2.789190e+00 |
| 25% | 16.000000 | 6.110000 | 66.900000 | 12.570000 | 0.500000 | 5.620000 | 0.160000 | 0.030000 | 750.000000 | 0.000000 | 0.000000 | 23.865000 | 20.072500 | 22.610000 | 5.715000 | 1.100000 | 3.475000 | -9.219701e-01 |
| 50% | 23.000000 | 8.000000 | 75.180000 | 15.610000 | 1.510000 | 6.210000 | 0.530000 | 0.110000 | 800.000000 | 0.210000 | 0.390000 | 31.475000 | 28.255000 | 30.065000 | 7.930000 | 1.520000 | 7.650000 | 2.862740e-01 |
| 75% | 31.000000 | 9.800000 | 77.710000 | 16.940000 | 5.330000 | 6.780000 | 0.900000 | 0.400000 | 850.000000 | 0.280000 | 1.050000 | 38.052500 | 36.530000 | 40.875000 | 10.000000 | 2.100000 | 15.125000 | 6.723379e-01 |
| max | 45.000000 | 27.000000 | 86.740000 | 26.450000 | 19.520000 | 8.660000 | 6.550000 | 4.200000 | 1108.000000 | 0.500000 | 4.700000 | 50.560000 | 59.040000 | 65.660000 | 22.000000 | 3.300000 | 91.430000 | 3.445228e+00 |
df['Biomass species'].value_counts().sort_index()
Biomass species 0 14 1 1 2 1 3 5 4 8 5 1 6 12 7 4 8 1 9 10 10 19 11 4 12 13 13 4 14 9 15 5 16 7 17 5 18 5 19 4 20 9 21 9 22 15 23 70 24 7 25 19 26 4 27 32 28 15 29 17 30 5 31 7 32 23 33 3 34 13 35 4 36 1 37 1 38 1 39 4 40 9 41 3 42 17 43 25 44 1 45 4 Name: count, dtype: int64
null_counts = df.groupby('Biomass species')[['CO', 'CO2', 'H2', 'CH4']].apply(lambda x: x.isnull().sum())
print(null_counts)
CO CO2 H2 CH4 Biomass species 0 0 0 0 0 1 0 0 0 0 2 1 1 1 1 3 0 0 0 0 4 0 0 0 0 5 0 0 0 0 6 0 0 0 0 7 4 4 4 4 8 0 0 0 0 9 0 0 0 0 10 0 0 0 0 11 0 0 0 0 12 0 0 0 0 13 0 0 0 0 14 0 0 0 0 15 0 0 0 0 16 0 0 0 0 17 0 0 0 0 18 0 0 0 0 19 4 4 4 4 20 0 0 0 0 21 0 0 0 0 22 0 0 0 0 23 0 0 0 0 24 0 0 0 0 25 0 0 0 0 26 0 0 0 0 27 0 0 0 0 28 0 0 0 0 29 0 0 0 0 30 0 0 0 0 31 0 0 0 0 32 0 0 0 0 33 0 0 0 0 34 0 0 0 0 35 0 0 0 0 36 0 0 0 0 37 0 0 0 0 38 0 0 0 0 39 0 0 0 0 40 0 0 0 0 41 0 0 0 0 42 8 8 8 8 43 18 18 18 18 44 1 1 1 1 45 0 0 0 0
strings_to_remove = ['Gas (m3/kg)', 'Tar (g/m^3)']
target_col = [item for item in target_col if item not in strings_to_remove]
df.drop(['Gas (m3/kg)', 'Tar (g/m^3)'], axis =1, inplace = True)
x_test = df[df['CO'].isna()]
# Remove those rows from the original DataFrame
df_up = df[~df['CO'].isna()]
x_test.info()
<class 'pandas.core.frame.DataFrame'> Index: 36 entries, 192 to 449 Data columns (total 17 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Biomass species 36 non-null int64 1 MC 36 non-null float64 2 VM 36 non-null float64 3 FC 36 non-null float64 4 Ash 36 non-null float64 5 C 36 non-null float64 6 H 36 non-null float64 7 O 36 non-null float64 8 N 36 non-null float64 9 S 36 non-null float64 10 oC 36 non-null float64 11 ER 36 non-null float64 12 S/B 36 non-null float64 13 CO 0 non-null float64 14 CO2 0 non-null float64 15 H2 0 non-null float64 16 CH4 0 non-null float64 dtypes: float64(16), int64(1) memory usage: 5.1 KB
df_up.info()
<class 'pandas.core.frame.DataFrame'> Index: 414 entries, 0 to 431 Data columns (total 17 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Biomass species 414 non-null int64 1 MC 414 non-null float64 2 VM 414 non-null float64 3 FC 414 non-null float64 4 Ash 414 non-null float64 5 C 414 non-null float64 6 H 414 non-null float64 7 O 414 non-null float64 8 N 414 non-null float64 9 S 414 non-null float64 10 oC 414 non-null float64 11 ER 414 non-null float64 12 S/B 414 non-null float64 13 CO 414 non-null float64 14 CO2 414 non-null float64 15 H2 414 non-null float64 16 CH4 414 non-null float64 dtypes: float64(16), int64(1) memory usage: 58.2 KB
category_counts = df_up['Biomass species'].value_counts()
# Separate rare (count == 1) and common (count >= 2) categories
rare_categories = category_counts[category_counts <= 2].index
common_categories = category_counts[category_counts > 2].index
# 1. Put rare category rows directly into x_train and y_train
df_rare = df_up[df_up['Biomass species'].isin(rare_categories)]
x_train_rare = df_rare.drop(columns=target_col)
y_train_rare = df_rare[target_col]
# 2. Use stratified split on the rest (common categories)
df_common = df_up[df_up['Biomass species'].isin(common_categories)]
X_common = df_common.drop(columns=target_col)
y_common = df_common[target_col]
x_train_common, x_val, y_train_common, y_val = train_test_split(
X_common, y_common, test_size=0.3, stratify=df_common['Biomass species'], random_state=42
)
# 3. Combine both parts into final x_train and y_train
x_train = pd.concat([x_train_common, x_train_rare]).reset_index(drop=True)
y_train = pd.concat([y_train_common, y_train_rare]).reset_index(drop=True)
print(x_train.shape, y_train.shape, x_val.shape, y_val.shape)
(291, 13) (291, 4) (123, 13) (123, 4)
sc = StandardScaler()
X_train_scaled = sc.fit_transform(x_train)
X_val_scaled = sc.transform(x_val)
y_val
| CO | CO2 | H2 | CH4 | |
|---|---|---|---|---|
| 373 | 32.08 | 43.56 | 17.80 | 6.56 |
| 166 | 16.16 | 25.51 | 52.13 | 6.20 |
| 277 | 29.40 | 32.45 | 32.58 | 5.57 |
| 61 | 16.24 | 27.77 | 51.93 | 4.06 |
| 230 | 19.80 | 10.89 | 54.46 | 14.85 |
| ... | ... | ... | ... | ... |
| 351 | 16.00 | 55.00 | 20.00 | 9.00 |
| 219 | 39.57 | 20.56 | 32.97 | 6.90 |
| 84 | 23.12 | 18.05 | 55.17 | 3.65 |
| 428 | 33.70 | 35.20 | 23.40 | 7.70 |
| 22 | 39.57 | 18.76 | 33.76 | 7.91 |
123 rows × 4 columns
lr = LinearRegression()
lr.fit(X_train_scaled, y_train)
y_pred_lr = lr.predict(X_val_scaled)
r2 = r2_score(y_val, y_pred_lr)
print(r2)
0.4642857004670947
from sklearn.multioutput import MultiOutputRegressor as MOR
svr = MOR(SVR(kernel='rbf', C = 100, gamma=0.1, epsilon =0.1))
svr.fit(X_train_scaled, y_train)
y_pred_svr = svr.predict(X_val_scaled)
r2 = r2_score(y_val, y_pred_svr)
r2
0.8597656722127263
rf = MOR(RandomForestRegressor(n_estimators = 100, random_state = 0))
rf.fit(X_train_scaled, y_train)
y_pred_rf = rf.predict(X_val_scaled)
r2 = r2_score(y_val, y_pred_rf)
r2
0.8593649636831314
xgbr = MOR(XGBRegressor())
xgbr.fit(X_train_scaled, y_train)
y_pred_xgbr = xgbr.predict(X_val_scaled)
r2 = r2_score(y_val, y_pred_xgbr)
r2
0.8437070181957863
catr = MOR(CatBoostRegressor(verbose = 0, iterations = 100))
catr.fit(X_train_scaled, y_train)
y_pred_catr = catr.predict(X_val_scaled)
r2 = r2_score(y_val, y_pred_catr)
r2
0.8757567617515191
ANN_model = Sequential([
Dense(32, input_dim=13), # No activation here
LeakyReLU(alpha=0.1), # LeakyReLU activation
Dense(32, activation='tanh'), # Tanh for richer non-linearity
Dense(16, activation='relu'), # ReLU for simplicity
Dense(1, activation='linear') # Linear for regression output
])
# Compile the model
ANN_model.compile(optimizer='adam',
loss='mean_squared_error',
metrics=['mae'])
# Train the model
ANN_model.fit(X_train_scaled, y_train.CO, epochs=500, verbose=1)
# Evaluate the model
loss, mae = ANN_model.evaluate(X_train_scaled, y_train.CO, verbose=0)
print(f"\nModel evaluation:\nLoss (MSE): {loss:.2f}, MAE: {mae:.2f}")
Epoch 1/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 3s 73ms/step - loss: 1049.5668 - mae: 31.1556 Epoch 2/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 990.8082 - mae: 30.2402 Epoch 3/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 997.6783 - mae: 30.2056 Epoch 4/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 946.1098 - mae: 29.4611 Epoch 5/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 875.7856 - mae: 28.1450 Epoch 6/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 812.5873 - mae: 27.0314 Epoch 7/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 784.5808 - mae: 26.4351 Epoch 8/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 629.8642 - mae: 23.3735 Epoch 9/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 604.7215 - mae: 22.6841 Epoch 10/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 474.7732 - mae: 19.9527 Epoch 11/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 386.4395 - mae: 17.4614 Epoch 12/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 305.8181 - mae: 15.1905 Epoch 13/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 233.2466 - mae: 12.9242 Epoch 14/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 193.7127 - mae: 11.7246 Epoch 15/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 150.3445 - mae: 10.4185 Epoch 16/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 121.2733 - mae: 9.2584 Epoch 17/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 106.8774 - mae: 8.6945 Epoch 18/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 90.4864 - mae: 8.0807 Epoch 19/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 87.8201 - mae: 7.8085 Epoch 20/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 74.9877 - mae: 7.2468 Epoch 21/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 76.6438 - mae: 7.2888 Epoch 22/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 73.5152 - mae: 7.1648 Epoch 23/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 71.0906 - mae: 6.9862 Epoch 24/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 73.8597 - mae: 7.1195 Epoch 25/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 64.4932 - mae: 6.5665 Epoch 26/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 68.5440 - mae: 6.7592 Epoch 27/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 68.2444 - mae: 6.7819 Epoch 28/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 54.3098 - mae: 5.9240 Epoch 29/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 66.5484 - mae: 6.6636 Epoch 30/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 59.9352 - mae: 6.1679 Epoch 31/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 58.0923 - mae: 6.1950 Epoch 32/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 59.9825 - mae: 6.2434 Epoch 33/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 64.6556 - mae: 6.3879 Epoch 34/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 59.0059 - mae: 6.1226 Epoch 35/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 55.5718 - mae: 5.8198 Epoch 36/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 57.1887 - mae: 5.9417 Epoch 37/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 55.4530 - mae: 6.0229 Epoch 38/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 57.8251 - mae: 6.1345 Epoch 39/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 57.2762 - mae: 6.0623 Epoch 40/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 58.1037 - mae: 6.1595 Epoch 41/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 57.7948 - mae: 6.0099 Epoch 42/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 59.4623 - mae: 6.0884 Epoch 43/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 58.5125 - mae: 6.0615 Epoch 44/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 60.8963 - mae: 6.2569 Epoch 45/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 55.7482 - mae: 6.0097 Epoch 46/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 50.2650 - mae: 5.6216 Epoch 47/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 52.2891 - mae: 5.7414 Epoch 48/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 56.5041 - mae: 5.9530 Epoch 49/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 50.2919 - mae: 5.5421 Epoch 50/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 49.1641 - mae: 5.5442 Epoch 51/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 45.6205 - mae: 5.2689 Epoch 52/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 48.7371 - mae: 5.5629 Epoch 53/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 48.2250 - mae: 5.5235 Epoch 54/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 50.6296 - mae: 5.5812 Epoch 55/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 42.4191 - mae: 5.1308 Epoch 56/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 43.2791 - mae: 5.1268 Epoch 57/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 46.3677 - mae: 5.4436 Epoch 58/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 47.5146 - mae: 5.3818 Epoch 59/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 45.9024 - mae: 5.3736 Epoch 60/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 45.0861 - mae: 5.2716 Epoch 61/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 43.4712 - mae: 5.1133 Epoch 62/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 39.0592 - mae: 4.8002 Epoch 63/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 43.6977 - mae: 5.2156 Epoch 64/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 40.8246 - mae: 4.9551 Epoch 65/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 39.5400 - mae: 4.8716 Epoch 66/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 40.3137 - mae: 4.9308 Epoch 67/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 40.6637 - mae: 4.9948 Epoch 68/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 46.0055 - mae: 5.3520 Epoch 69/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 37.6940 - mae: 4.8441 Epoch 70/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 38.4222 - mae: 4.9042 Epoch 71/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 37.4353 - mae: 4.7259 Epoch 72/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 35.1792 - mae: 4.6057 Epoch 73/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 39.6806 - mae: 4.7793 Epoch 74/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 38.8105 - mae: 4.8041 Epoch 75/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 35.2362 - mae: 4.5829 Epoch 76/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 35.0303 - mae: 4.5737 Epoch 77/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 36.8052 - mae: 4.7044 Epoch 78/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 32.3093 - mae: 4.4273 Epoch 79/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 31.5110 - mae: 4.3561 Epoch 80/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 37.6505 - mae: 4.7359 Epoch 81/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 32.3553 - mae: 4.5196 Epoch 82/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 31.5304 - mae: 4.4196 Epoch 83/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 28.1999 - mae: 4.1188 Epoch 84/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 35.5382 - mae: 4.6962 Epoch 85/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 32.0853 - mae: 4.3008 Epoch 86/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 29.7146 - mae: 4.2753 Epoch 87/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 28.1309 - mae: 4.0398 Epoch 88/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 31.4374 - mae: 4.2591 Epoch 89/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 29.4820 - mae: 4.1664 Epoch 90/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 26.1225 - mae: 3.8892 Epoch 91/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 27.2259 - mae: 3.9730 Epoch 92/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 24.5490 - mae: 3.7907 Epoch 93/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 27.6922 - mae: 4.0293 Epoch 94/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 25.6301 - mae: 3.9065 Epoch 95/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 26.7401 - mae: 3.9771 Epoch 96/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 27.9769 - mae: 3.9282 Epoch 97/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 25.4226 - mae: 3.8476 Epoch 98/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 22.9323 - mae: 3.5304 Epoch 99/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 27.3618 - mae: 3.9609 Epoch 100/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 24.9963 - mae: 3.7854 Epoch 101/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 24.6444 - mae: 3.7258 Epoch 102/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 22.9058 - mae: 3.5608 Epoch 103/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 21.8688 - mae: 3.5154 Epoch 104/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 27.2191 - mae: 3.9576 Epoch 105/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 28.1785 - mae: 3.8518 Epoch 106/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 22.8801 - mae: 3.5336 Epoch 107/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 24.9918 - mae: 3.6878 Epoch 108/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 19.1551 - mae: 3.3090 Epoch 109/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 22.7038 - mae: 3.5484 Epoch 110/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 22.0876 - mae: 3.5077 Epoch 111/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 24.2354 - mae: 3.6588 Epoch 112/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 23.2939 - mae: 3.6849 Epoch 113/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 21.6588 - mae: 3.4449 Epoch 114/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 19.5717 - mae: 3.2436 Epoch 115/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 19.8740 - mae: 3.3301 Epoch 116/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 19.3815 - mae: 3.2860 Epoch 117/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 21.1956 - mae: 3.4279 Epoch 118/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 19.5456 - mae: 3.3085 Epoch 119/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 22.0018 - mae: 3.3917 Epoch 120/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 21.5345 - mae: 3.3947 Epoch 121/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 18.6902 - mae: 3.1535 Epoch 122/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 20.5010 - mae: 3.3366 Epoch 123/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 20.4733 - mae: 3.2849 Epoch 124/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 21.2049 - mae: 3.3962 Epoch 125/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 17.7079 - mae: 3.0630 Epoch 126/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 17.8997 - mae: 3.0776 Epoch 127/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 17.1731 - mae: 2.9640 Epoch 128/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 17.4506 - mae: 3.0995 Epoch 129/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 18.2497 - mae: 3.0618 Epoch 130/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 20.5432 - mae: 3.2767 Epoch 131/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 17.9494 - mae: 3.1669 Epoch 132/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 15.6547 - mae: 2.9171 Epoch 133/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 16.7346 - mae: 3.0229 Epoch 134/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 18.9137 - mae: 3.1245 Epoch 135/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 19.5400 - mae: 3.3215 Epoch 136/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 19.6561 - mae: 3.2664 Epoch 137/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 17.0565 - mae: 3.0045 Epoch 138/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 16.9809 - mae: 3.1107 Epoch 139/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 18.3218 - mae: 3.1368 Epoch 140/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 17.4129 - mae: 3.0626 Epoch 141/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 17.9193 - mae: 3.0643 Epoch 142/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 18.4840 - mae: 3.1768 Epoch 143/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 17.2585 - mae: 3.0968 Epoch 144/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 18.4544 - mae: 3.1746 Epoch 145/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 17.4295 - mae: 3.0693 Epoch 146/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 17.2559 - mae: 3.0132 Epoch 147/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 16.7293 - mae: 2.9369 Epoch 148/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 18.7350 - mae: 3.1985 Epoch 149/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 19.5996 - mae: 3.1382 Epoch 150/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 16.1582 - mae: 2.9745 Epoch 151/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.4522 - mae: 2.7548 Epoch 152/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 15.2458 - mae: 2.8222 Epoch 153/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.9354 - mae: 2.7755 Epoch 154/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 15.5355 - mae: 2.8087 Epoch 155/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.6071 - mae: 2.6840 Epoch 156/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 15.7906 - mae: 2.8778 Epoch 157/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 16.3160 - mae: 2.9861 Epoch 158/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 13.5215 - mae: 2.6492 Epoch 159/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.7714 - mae: 2.8399 Epoch 160/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 17.2373 - mae: 2.9664 Epoch 161/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 16.0783 - mae: 2.9210 Epoch 162/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.8620 - mae: 2.7460 Epoch 163/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 15.6127 - mae: 2.8041 Epoch 164/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 18.4618 - mae: 3.0441 Epoch 165/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 15.2178 - mae: 2.7968 Epoch 166/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.6241 - mae: 2.7654 Epoch 167/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.4566 - mae: 2.5760 Epoch 168/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 15.8734 - mae: 2.8540 Epoch 169/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 13.5075 - mae: 2.7120 Epoch 170/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 15.3931 - mae: 2.9061 Epoch 171/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 13.7793 - mae: 2.6955 Epoch 172/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 18.2681 - mae: 3.0696 Epoch 173/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 13.7597 - mae: 2.6869 Epoch 174/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 13.9697 - mae: 2.7104 Epoch 175/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 15.3552 - mae: 2.8191 Epoch 176/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.9031 - mae: 2.6247 Epoch 177/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 15.6405 - mae: 2.7684 Epoch 178/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.6220 - mae: 2.6508 Epoch 179/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.4352 - mae: 2.6886 Epoch 180/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 15.2032 - mae: 2.7976 Epoch 181/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 13.1606 - mae: 2.5459 Epoch 182/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.9826 - mae: 2.5154 Epoch 183/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.3524 - mae: 2.6852 Epoch 184/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.3347 - mae: 2.7753 Epoch 185/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 13.7059 - mae: 2.6754 Epoch 186/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 16.7382 - mae: 2.9608 Epoch 187/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 13.5628 - mae: 2.5736 Epoch 188/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.1552 - mae: 2.3583 Epoch 189/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.8257 - mae: 2.5482 Epoch 190/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.6458 - mae: 2.7296 Epoch 191/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 13.6867 - mae: 2.6346 Epoch 192/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.4644 - mae: 2.5646 Epoch 193/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.8776 - mae: 2.4346 Epoch 194/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.2379 - mae: 2.3383 Epoch 195/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.0399 - mae: 2.6771 Epoch 196/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 15.1619 - mae: 2.7304 Epoch 197/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.3813 - mae: 2.3802 Epoch 198/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.4387 - mae: 2.3931 Epoch 199/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.2842 - mae: 2.4844 Epoch 200/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.9771 - 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loss: 11.2264 - mae: 2.3728 Epoch 214/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.9747 - mae: 2.3688 Epoch 215/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.0189 - mae: 2.3585 Epoch 216/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.6053 - mae: 2.2643 Epoch 217/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.4804 - mae: 2.3079 Epoch 218/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.6690 - mae: 2.3054 Epoch 219/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.1003 - mae: 2.2849 Epoch 220/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.7362 - mae: 2.3402 Epoch 221/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.7143 - mae: 2.2660 Epoch 222/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.1649 - mae: 2.4281 Epoch 223/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.4735 - mae: 2.3397 Epoch 224/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.9349 - mae: 2.2391 Epoch 225/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.4857 - mae: 2.2126 Epoch 226/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.8658 - mae: 2.0347 Epoch 227/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.4893 - mae: 2.2164 Epoch 228/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.8990 - mae: 2.2382 Epoch 229/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.6970 - mae: 2.0715 Epoch 230/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.2404 - mae: 2.1493 Epoch 231/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.6201 - mae: 2.2906 Epoch 232/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.9761 - mae: 2.2215 Epoch 233/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.1878 - mae: 2.1658 Epoch 234/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.4778 - mae: 2.0545 Epoch 235/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.0767 - mae: 2.1459 Epoch 236/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.6867 - mae: 2.3378 Epoch 237/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.2053 - mae: 2.1621 Epoch 238/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.1747 - mae: 2.0918 Epoch 239/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.1489 - mae: 2.1063 Epoch 240/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.0646 - mae: 2.0497 Epoch 241/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.3317 - mae: 2.0287 Epoch 242/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.1991 - mae: 2.3062 Epoch 243/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.7304 - mae: 2.0722 Epoch 244/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.0453 - mae: 1.9254 Epoch 245/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.8318 - mae: 1.9011 Epoch 246/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.1199 - mae: 2.1026 Epoch 247/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.7387 - mae: 1.9115 Epoch 248/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.8999 - mae: 2.2430 Epoch 249/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.3109 - mae: 2.0055 Epoch 250/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.0610 - 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loss: 8.1631 - mae: 1.9030 Epoch 264/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.2763 - mae: 1.8540 Epoch 265/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.5749 - mae: 2.0115 Epoch 266/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.6657 - mae: 1.9886 Epoch 267/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.2563 - mae: 1.9849 Epoch 268/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.2689 - mae: 2.0956 Epoch 269/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.9055 - mae: 1.9312 Epoch 270/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.6906 - mae: 1.9254 Epoch 271/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.8792 - mae: 1.8277 Epoch 272/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.5770 - mae: 1.7990 Epoch 273/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.0742 - mae: 1.8560 Epoch 274/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.0556 - mae: 1.9480 Epoch 275/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.7471 - mae: 1.8695 Epoch 276/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - 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loss: 6.6704 - mae: 1.7434 Epoch 329/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.3001 - mae: 1.8465 Epoch 330/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.8150 - mae: 1.9558 Epoch 331/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.2932 - mae: 1.7034 Epoch 332/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.2603 - mae: 1.7853 Epoch 333/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.5499 - mae: 1.6925 Epoch 334/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 8.7210 - mae: 1.9415 Epoch 335/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.9925 - mae: 1.6494 Epoch 336/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.2069 - mae: 1.8744 Epoch 337/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.6790 - mae: 1.6815 Epoch 338/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.3123 - mae: 1.7971 Epoch 339/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.2852 - mae: 1.5632 Epoch 340/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.6461 - mae: 1.5981 Epoch 341/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - 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loss: 6.0648 - mae: 1.4644 Epoch 433/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.3057 - mae: 1.5018 Epoch 434/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.8582 - mae: 1.4008 Epoch 435/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.8165 - mae: 1.4377 Epoch 436/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.1185 - mae: 1.4699 Epoch 437/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.5749 - mae: 1.5116 Epoch 438/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.9733 - mae: 1.5844 Epoch 439/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.4861 - mae: 1.4467 Epoch 440/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.8616 - mae: 1.3272 Epoch 441/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.1900 - mae: 1.6252 Epoch 442/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.5820 - mae: 1.3972 Epoch 443/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.0918 - mae: 1.4767 Epoch 444/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.3299 - mae: 1.5987 Epoch 445/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.7869 - mae: 1.5129 Epoch 446/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.9886 - mae: 1.5265 Epoch 447/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.6581 - mae: 1.4699 Epoch 448/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.6576 - mae: 1.5441 Epoch 449/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.1280 - mae: 1.4364 Epoch 450/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.6271 - mae: 1.5258 Epoch 451/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.9385 - mae: 1.4117 Epoch 452/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.5694 - mae: 1.3875 Epoch 453/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.5401 - mae: 1.4993 Epoch 454/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.9672 - mae: 1.4045 Epoch 455/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.9416 - mae: 1.4278 Epoch 456/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.3139 - mae: 1.4943 Epoch 457/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.4715 - mae: 1.4818 Epoch 458/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.4714 - mae: 1.3555 Epoch 459/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.6679 - mae: 1.3837 Epoch 460/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.3692 - mae: 1.4775 Epoch 461/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.9616 - mae: 1.4848 Epoch 462/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.2197 - mae: 1.4827 Epoch 463/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.6465 - mae: 1.3731 Epoch 464/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.1985 - mae: 1.4664 Epoch 465/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.8643 - mae: 1.4402 Epoch 466/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.3455 - mae: 1.4653 Epoch 467/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.3018 - mae: 1.5030 Epoch 468/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.1616 - mae: 1.5486 Epoch 469/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.8834 - mae: 1.3952 Epoch 470/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.8562 - mae: 1.4746 Epoch 471/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.5395 - mae: 1.4132 Epoch 472/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.8587 - mae: 1.3960 Epoch 473/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.0071 - mae: 1.3938 Epoch 474/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.6593 - mae: 1.3721 Epoch 475/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.1358 - mae: 1.4420 Epoch 476/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.3344 - mae: 1.4666 Epoch 477/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.2943 - mae: 1.3883 Epoch 478/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.1035 - mae: 1.3060 Epoch 479/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.8962 - mae: 1.3939 Epoch 480/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.9681 - mae: 1.2708 Epoch 481/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.0422 - mae: 1.4280 Epoch 482/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.0126 - mae: 1.3127 Epoch 483/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.0385 - mae: 1.5372 Epoch 484/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.0688 - mae: 1.3264 Epoch 485/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.0267 - mae: 1.4433 Epoch 486/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.7391 - mae: 1.4071 Epoch 487/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.9989 - mae: 1.4683 Epoch 488/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.5448 - mae: 1.6161 Epoch 489/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.2658 - mae: 1.4413 Epoch 490/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.3288 - mae: 1.3699 Epoch 491/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.3687 - mae: 1.3123 Epoch 492/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.3377 - mae: 1.4158 Epoch 493/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.4964 - mae: 1.3429 Epoch 494/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.1610 - mae: 1.3191 Epoch 495/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.9518 - mae: 1.0940 Epoch 496/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.3821 - mae: 1.4393 Epoch 497/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.3060 - mae: 1.3377 Epoch 498/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.7516 - mae: 1.3700 Epoch 499/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.3595 - mae: 1.3024 Epoch 500/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.8172 - mae: 1.5220 Model evaluation: Loss (MSE): 4.56, MAE: 1.35
models = {
"Linear Regression": LinearRegression(),
"Lasso": Lasso(),
"K-Neighbors Regressor": KNeighborsRegressor(),
"Decision Tree": DecisionTreeRegressor(),
"Random Forest Regressor": RandomForestRegressor(),
"Gradient Boosting": GradientBoostingRegressor(),
"XGBRegressor": XGBRegressor(),
"CatBoosting Regressor": CatBoostRegressor(verbose=0, iterations = 100),
"AdaBoost Regressor": AdaBoostRegressor(),
"ExtraTreesRegressor": ExtraTreesRegressor(),
"Support Vector Regressor(RBF)": SVR(kernel="rbf"),
"Support Vector Regressor(linear)": SVR(kernel="linear"),
"Nu SVR(rbf)": NuSVR(kernel="rbf"),
"ANN": ANN_model
}
def safe_flatten(y_pred):
"""
Flattens the array if it's a 2D array with shape (n, 1).
Useful for ANN predictions.
"""
if isinstance(y_pred, (np.ndarray, list)) and len(np.shape(y_pred)) == 2 and y_pred.shape[1] == 1:
return y_pred.flatten()
return y_pred
r2_train_score = {}
r2_test_score = {}
def evaluate_model(models, X_train, y_train, X_val, y_val):
for model_name, model in models.items():
model.fit(X_train, y_train)
y_train_pred = model.predict(X_train)
y_test_pred = model.predict(X_val)
y = y_val
y_pred = safe_flatten(y_test_pred)
plt.figure(figsize=(8, 6))
r2 = r2_score(y, y_pred)
sns.scatterplot(x=y, y=y_pred, label='Predicted vs Actual', color='blue')
sns.regplot(x=y, y=y_pred, scatter=False, label='Regression Line', color='red', ci=None)
plt.xlabel('Actual Values')
plt.ylabel('Predicted Values')
plt.title(f'CO Actual vs Predicted Values (R² Score: {r2:.4f}) for {model_name} model')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.savefig(f'CO Actual vs Predicted Values (R² Score: {r2:.4f}) for {model_name} model.png')
plt.show()
r2_train_score[model_name] = r2_score(y_train, y_train_pred)
r2_test_score[model_name] = r2_score(y_val, y_test_pred)
evaluate_model(models, X_train_scaled, y_train.CO, X_val_scaled, y_val.CO)
10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.2083 - mae: 1.4006 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step 4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 81ms/step
score = pd.DataFrame(list(zip(models.keys(), r2_train_score.values(), r2_test_score.values())), columns=["Model","r2_train_score", "r2_test_score"])
score
| Model | r2_train_score | r2_test_score | |
|---|---|---|---|
| 0 | Linear Regression | 0.298496 | 0.310329 |
| 1 | Lasso | 0.205461 | 0.198758 |
| 2 | K-Neighbors Regressor | 0.794460 | 0.706655 |
| 3 | Decision Tree | 0.997006 | 0.731715 |
| 4 | Random Forest Regressor | 0.970487 | 0.827927 |
| 5 | Gradient Boosting | 0.919220 | 0.797646 |
| 6 | XGBRegressor | 0.996926 | 0.788515 |
| 7 | CatBoosting Regressor | 0.968410 | 0.845676 |
| 8 | AdaBoost Regressor | 0.746776 | 0.719574 |
| 9 | ExtraTreesRegressor | 0.997006 | 0.844978 |
| 10 | Support Vector Regressor(RBF) | 0.473190 | 0.456430 |
| 11 | Support Vector Regressor(linear) | 0.253874 | 0.261680 |
| 12 | Nu SVR(rbf) | 0.433602 | 0.404798 |
| 13 | ANN | 0.943367 | 0.822314 |
# Set positions
x = np.arange(len(score['Model']))
width = 0.35 # Width of the bars
# Create plot
fig, ax = plt.subplots(figsize=(16, 8))
bars1 = ax.bar(x - width/2, score['r2_train_score'], width, label='Train R²', color='skyblue')
bars2 = ax.bar(x + width/2, score['r2_test_score'], width, label='Validation R²', color='salmon')
# Add labels and title
ax.set_xlabel('ML Models')
ax.set_ylabel('R² Score')
ax.set_title('CO Train vs Validation R² Score for Different Models')
ax.set_xticks(x)
ax.set_xticklabels(score['Model'], rotation=45)
ax.legend()
# Add R2 score text on top of bars
for bar in bars1 + bars2:
yval = bar.get_height()
ax.text(bar.get_x() + bar.get_width()/2.0, yval + 0.01, f'{yval:.2f}', ha='center', va='bottom')
plt.tight_layout()
plt.savefig("CO Train vs Validation R² Score for Different Models")
plt.show()
ANN_model = Sequential([
Dense(32, input_dim=13), # No activation here
LeakyReLU(alpha=0.1), # LeakyReLU activation
Dense(32, activation='tanh'), # Tanh for richer non-linearity
Dense(16, activation='relu'), # ReLU for simplicity
Dense(1, activation='linear') # Linear for regression output
])
# Compile the model
ANN_model.compile(optimizer='adam',
loss='mean_squared_error',
metrics=['mae'])
# Train the model
ANN_model.fit(X_train_scaled, y_train.CO2, epochs=500, verbose=1)
# Evaluate the model
loss, mae = ANN_model.evaluate(X_train_scaled, y_train.CO2, verbose=0)
print(f"\nModel evaluation:\nLoss (MSE): {loss:.2f}, MAE: {mae:.2f}")
Epoch 1/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 2s 35ms/step - loss: 971.2888 - mae: 29.2199 Epoch 2/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 852.4738 - mae: 27.1535 Epoch 3/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 897.0617 - mae: 27.5853 Epoch 4/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 816.9807 - mae: 26.5347 Epoch 5/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 758.8616 - mae: 25.5917 Epoch 6/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 720.9613 - mae: 24.7938 Epoch 7/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 647.0811 - mae: 23.1684 Epoch 8/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 618.7953 - mae: 22.4222 Epoch 9/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 553.5217 - mae: 21.1887 Epoch 10/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 463.5688 - mae: 18.8825 Epoch 11/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 391.7823 - mae: 17.1454 Epoch 12/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 332.0067 - mae: 15.5609 Epoch 13/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 268.2933 - mae: 13.7168 Epoch 14/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 217.2852 - mae: 12.0998 Epoch 15/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 192.3208 - mae: 11.0861 Epoch 16/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 163.3085 - mae: 10.0014 Epoch 17/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 148.8390 - mae: 9.6554 Epoch 18/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 129.5284 - mae: 8.8581 Epoch 19/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 111.0075 - mae: 8.3567 Epoch 20/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 109.2474 - mae: 8.3348 Epoch 21/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 111.4418 - mae: 8.3495 Epoch 22/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 100.5414 - mae: 8.0990 Epoch 23/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 87.0583 - mae: 7.3503 Epoch 24/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 91.5189 - mae: 7.5758 Epoch 25/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 86.7589 - mae: 7.2348 Epoch 26/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 80.0594 - mae: 6.9449 Epoch 27/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 79.8378 - mae: 6.9428 Epoch 28/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 78.9295 - mae: 6.8522 Epoch 29/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 68.9338 - mae: 6.3111 Epoch 30/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 71.8124 - mae: 6.5123 Epoch 31/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 69.4678 - mae: 6.3512 Epoch 32/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 70.8776 - mae: 6.6940 Epoch 33/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 61.6333 - mae: 6.1062 Epoch 34/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 61.0036 - mae: 6.1099 Epoch 35/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 58.2509 - mae: 5.9578 Epoch 36/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 60.7742 - mae: 6.0740 Epoch 37/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 60.5848 - mae: 6.0946 Epoch 38/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 59.7367 - mae: 6.1034 Epoch 39/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 56.5720 - mae: 5.7739 Epoch 40/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 51.8915 - mae: 5.7104 Epoch 41/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 47.9939 - mae: 5.3694 Epoch 42/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 51.5936 - mae: 5.5273 Epoch 43/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 49.7496 - mae: 5.5159 Epoch 44/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 46.5658 - mae: 5.2398 Epoch 45/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 51.0932 - mae: 5.5899 Epoch 46/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 50.0542 - mae: 5.4831 Epoch 47/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 42.8194 - mae: 5.0390 Epoch 48/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 44.5907 - mae: 5.0486 Epoch 49/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 40.4311 - mae: 4.8723 Epoch 50/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 41.4721 - mae: 4.8772 Epoch 51/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 42.0994 - mae: 4.8855 Epoch 52/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 37.5851 - mae: 4.5207 Epoch 53/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 34.4494 - mae: 4.3824 Epoch 54/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 37.7001 - mae: 4.6922 Epoch 55/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 32.5776 - mae: 4.2685 Epoch 56/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 37.6299 - mae: 4.5828 Epoch 57/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 32.6874 - mae: 4.2509 Epoch 58/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 32.1981 - mae: 4.1700 Epoch 59/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 30.9978 - mae: 4.0709 Epoch 60/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 32.9333 - mae: 4.3929 Epoch 61/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 32.1816 - mae: 4.3212 Epoch 62/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 29.9763 - mae: 4.0720 Epoch 63/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 30.6256 - mae: 4.1386 Epoch 64/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 30.9484 - mae: 4.0547 Epoch 65/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 31.5012 - mae: 4.1652 Epoch 66/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 30.0958 - mae: 4.1425 Epoch 67/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 29.2075 - mae: 4.0200 Epoch 68/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 24.8771 - mae: 3.7245 Epoch 69/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 25.5529 - mae: 3.7960 Epoch 70/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 27.2648 - mae: 3.8462 Epoch 71/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 24.0162 - mae: 3.6635 Epoch 72/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 27.9976 - mae: 3.8432 Epoch 73/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 27.6230 - mae: 3.9056 Epoch 74/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 25.1281 - mae: 3.7212 Epoch 75/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 24.9558 - mae: 3.6680 Epoch 76/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 25.2152 - mae: 3.6010 Epoch 77/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 24.6198 - mae: 3.6775 Epoch 78/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 24.1574 - mae: 3.5959 Epoch 79/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 23.8615 - mae: 3.5766 Epoch 80/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 21.8738 - mae: 3.5123 Epoch 81/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 19.7147 - mae: 3.3153 Epoch 82/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 21.0796 - mae: 3.4543 Epoch 83/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 24.8496 - mae: 3.5379 Epoch 84/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 24.4565 - mae: 3.5608 Epoch 85/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 20.3060 - mae: 3.3647 Epoch 86/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 21.5074 - mae: 3.4104 Epoch 87/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 21.8676 - mae: 3.4403 Epoch 88/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 21.6501 - mae: 3.4304 Epoch 89/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 22.4576 - mae: 3.6158 Epoch 90/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 18.9229 - mae: 3.1636 Epoch 91/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 20.2548 - mae: 3.3057 Epoch 92/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 22.4314 - mae: 3.3893 Epoch 93/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 21.8139 - mae: 3.4199 Epoch 94/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 19.8418 - mae: 3.2225 Epoch 95/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 20.1343 - mae: 3.2271 Epoch 96/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 21.3820 - mae: 3.3501 Epoch 97/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 21.2544 - mae: 3.3067 Epoch 98/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 20.9168 - mae: 3.1676 Epoch 99/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 18.2320 - mae: 3.0297 Epoch 100/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 17.2101 - mae: 2.9583 Epoch 101/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 18.3682 - mae: 3.0407 Epoch 102/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 21.3420 - mae: 3.1874 Epoch 103/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 18.1227 - mae: 3.0070 Epoch 104/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 17.2004 - mae: 2.9861 Epoch 105/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 21.6553 - mae: 3.2881 Epoch 106/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 19.2212 - mae: 3.0393 Epoch 107/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 18.5478 - mae: 3.0555 Epoch 108/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 15.9787 - mae: 2.8014 Epoch 109/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 16.3722 - mae: 2.9790 Epoch 110/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 17.9630 - mae: 2.9683 Epoch 111/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 17.1403 - mae: 2.9139 Epoch 112/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 16.7631 - mae: 2.8454 Epoch 113/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 17.0715 - mae: 2.8679 Epoch 114/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.9353 - mae: 2.7678 Epoch 115/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.5872 - mae: 2.6647 Epoch 116/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 18.8832 - mae: 2.9775 Epoch 117/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 18.1620 - mae: 2.9049 Epoch 118/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 16.4629 - mae: 2.7852 Epoch 119/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 15.3947 - mae: 2.7458 Epoch 120/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 18.2059 - mae: 2.9202 Epoch 121/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.6663 - mae: 2.7972 Epoch 122/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 15.6063 - mae: 2.8079 Epoch 123/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 15.0874 - mae: 2.7518 Epoch 124/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 16.0720 - mae: 2.7614 Epoch 125/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.9881 - mae: 2.6036 Epoch 126/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 17.1759 - mae: 2.8811 Epoch 127/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.9151 - mae: 2.7088 Epoch 128/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 13.1569 - mae: 2.5561 Epoch 129/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 15.2690 - mae: 2.6289 Epoch 130/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 13.9103 - mae: 2.7369 Epoch 131/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.0609 - mae: 2.6313 Epoch 132/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 13.4980 - mae: 2.5778 Epoch 133/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 18.2210 - mae: 2.8224 Epoch 134/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 15.4969 - mae: 2.7157 Epoch 135/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.0033 - mae: 2.5776 Epoch 136/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.7156 - mae: 2.6401 Epoch 137/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.5318 - mae: 2.4864 Epoch 138/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.9124 - mae: 2.5416 Epoch 139/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 13.7044 - mae: 2.5440 Epoch 140/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 13.0762 - mae: 2.5628 Epoch 141/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.2069 - mae: 2.3918 Epoch 142/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 15.0423 - mae: 2.5190 Epoch 143/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 13.3621 - mae: 2.4933 Epoch 144/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.5408 - mae: 2.6112 Epoch 145/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 13.4501 - mae: 2.5153 Epoch 146/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.4330 - mae: 2.5991 Epoch 147/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 15.7644 - mae: 2.6010 Epoch 148/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 17.3841 - mae: 2.7626 Epoch 149/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 15.6525 - mae: 2.6328 Epoch 150/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.1399 - mae: 2.4120 Epoch 151/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 13.3692 - mae: 2.5149 Epoch 152/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 13.7281 - mae: 2.5431 Epoch 153/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.6275 - mae: 2.5051 Epoch 154/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.4432 - mae: 2.2807 Epoch 155/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.9748 - mae: 2.3453 Epoch 156/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 13.0870 - mae: 2.4460 Epoch 157/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.8200 - mae: 2.4027 Epoch 158/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 16.0411 - mae: 2.6307 Epoch 159/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.3464 - mae: 2.3642 Epoch 160/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.6200 - mae: 2.4659 Epoch 161/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 13.4703 - mae: 2.4894 Epoch 162/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.5924 - mae: 2.4790 Epoch 163/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.8639 - mae: 2.2974 Epoch 164/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.4208 - mae: 2.1181 Epoch 165/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.0252 - mae: 2.4600 Epoch 166/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.9212 - mae: 2.1351 Epoch 167/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.3777 - mae: 2.3104 Epoch 168/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.3606 - mae: 2.3175 Epoch 169/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.8797 - mae: 2.4185 Epoch 170/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.9267 - mae: 2.3343 Epoch 171/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.4147 - mae: 2.2555 Epoch 172/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.4776 - mae: 2.2484 Epoch 173/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.1689 - mae: 2.2583 Epoch 174/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.5471 - mae: 2.1853 Epoch 175/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.5190 - mae: 2.2610 Epoch 176/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.3570 - mae: 2.2011 Epoch 177/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.9849 - mae: 2.1909 Epoch 178/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 13.0885 - mae: 2.4585 Epoch 179/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.8020 - mae: 2.2282 Epoch 180/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.2670 - mae: 2.0816 Epoch 181/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.3236 - mae: 2.3666 Epoch 182/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.7998 - mae: 2.1369 Epoch 183/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.5325 - mae: 2.2095 Epoch 184/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.1959 - mae: 2.2341 Epoch 185/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.3149 - mae: 1.9905 Epoch 186/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.9661 - mae: 2.1494 Epoch 187/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.4774 - mae: 2.0671 Epoch 188/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.7935 - mae: 2.1519 Epoch 189/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.0832 - mae: 2.0469 Epoch 190/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.5842 - mae: 2.2187 Epoch 191/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.2557 - mae: 2.2303 Epoch 192/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.4438 - mae: 2.4122 Epoch 193/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.6123 - mae: 2.1208 Epoch 194/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.2835 - mae: 2.0863 Epoch 195/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.1685 - mae: 2.0193 Epoch 196/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.4882 - mae: 2.0940 Epoch 197/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.7595 - mae: 2.2450 Epoch 198/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.4376 - mae: 2.1344 Epoch 199/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.4817 - mae: 2.0300 Epoch 200/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.4764 - 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loss: 8.5481 - mae: 1.9807 Epoch 214/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.7472 - mae: 2.0314 Epoch 215/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.6581 - mae: 2.0228 Epoch 216/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.5184 - mae: 2.1394 Epoch 217/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.8058 - mae: 2.0452 Epoch 218/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.0009 - mae: 2.0898 Epoch 219/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.0937 - mae: 1.9801 Epoch 220/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.2267 - mae: 1.9356 Epoch 221/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.5747 - mae: 1.9111 Epoch 222/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.4456 - mae: 1.9373 Epoch 223/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.2775 - mae: 2.0597 Epoch 224/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.8541 - mae: 1.9812 Epoch 225/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.2441 - mae: 2.0272 Epoch 226/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.5037 - mae: 1.9547 Epoch 227/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.4962 - mae: 1.8930 Epoch 228/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.2874 - mae: 2.1212 Epoch 229/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.4717 - mae: 1.8514 Epoch 230/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.8355 - mae: 1.8915 Epoch 231/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.3750 - mae: 2.0916 Epoch 232/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.1490 - mae: 1.8906 Epoch 233/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.3002 - mae: 1.9579 Epoch 234/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.7984 - mae: 2.0958 Epoch 235/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.1692 - mae: 1.9182 Epoch 236/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.9161 - mae: 1.8850 Epoch 237/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.0815 - mae: 1.7902 Epoch 238/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.9475 - mae: 1.8997 Epoch 239/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.1444 - mae: 1.8783 Epoch 240/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.5430 - mae: 1.7544 Epoch 241/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.2055 - mae: 1.9481 Epoch 242/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.3015 - mae: 1.8329 Epoch 243/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.6849 - mae: 1.8838 Epoch 244/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.4790 - mae: 1.8495 Epoch 245/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.7190 - mae: 1.7190 Epoch 246/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.8855 - mae: 1.7971 Epoch 247/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.3038 - mae: 1.8482 Epoch 248/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.7046 - mae: 1.8109 Epoch 249/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.4530 - mae: 2.0775 Epoch 250/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.8558 - mae: 1.8351 Epoch 251/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.9916 - mae: 1.8844 Epoch 252/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.4748 - mae: 1.7468 Epoch 253/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.2290 - mae: 1.7803 Epoch 254/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.4913 - mae: 1.9282 Epoch 255/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.8452 - mae: 1.7730 Epoch 256/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.0628 - mae: 2.0318 Epoch 257/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.4110 - mae: 1.7346 Epoch 258/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.6398 - mae: 1.7761 Epoch 259/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.6387 - mae: 1.8866 Epoch 260/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.6706 - mae: 1.7465 Epoch 261/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.8878 - mae: 1.7770 Epoch 262/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.0254 - mae: 1.6201 Epoch 263/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.8832 - mae: 1.7164 Epoch 264/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.2701 - mae: 1.6343 Epoch 265/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.8577 - mae: 1.6951 Epoch 266/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.3830 - mae: 1.6601 Epoch 267/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.4352 - mae: 1.8036 Epoch 268/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.6837 - mae: 1.5982 Epoch 269/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.6979 - mae: 1.8353 Epoch 270/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.5122 - mae: 1.8066 Epoch 271/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.5986 - mae: 1.9211 Epoch 272/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.8995 - mae: 1.6526 Epoch 273/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.9973 - mae: 1.6490 Epoch 274/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.4369 - mae: 1.8217 Epoch 275/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.9303 - mae: 1.7830 Epoch 276/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.5646 - mae: 1.5765 Epoch 277/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.2120 - mae: 1.7956 Epoch 278/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.3433 - mae: 1.6544 Epoch 279/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.2514 - mae: 1.7489 Epoch 280/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.5568 - mae: 1.5493 Epoch 281/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.0587 - mae: 1.7983 Epoch 282/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.0866 - mae: 1.6224 Epoch 283/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.4047 - mae: 1.6550 Epoch 284/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.4559 - mae: 1.5546 Epoch 285/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.5079 - mae: 1.7009 Epoch 286/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.3476 - mae: 1.6061 Epoch 287/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.7504 - mae: 1.7274 Epoch 288/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.5816 - mae: 1.5574 Epoch 289/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - 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loss: 5.8498 - mae: 1.5372 Epoch 342/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.0900 - mae: 1.3510 Epoch 343/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.1917 - mae: 1.3814 Epoch 344/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.5951 - mae: 1.6742 Epoch 345/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.3282 - mae: 1.5771 Epoch 346/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.1270 - mae: 1.5676 Epoch 347/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.8063 - mae: 1.4751 Epoch 348/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.1419 - mae: 1.4780 Epoch 349/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.6075 - mae: 1.4363 Epoch 350/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.9445 - mae: 1.6153 Epoch 351/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.9041 - mae: 1.4446 Epoch 352/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.3984 - mae: 1.4507 Epoch 353/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.5721 - mae: 1.3950 Epoch 354/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - 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loss: 3.4405 - mae: 1.2523 Epoch 446/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.2674 - mae: 1.2074 Epoch 447/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.3782 - mae: 1.2488 Epoch 448/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.4201 - mae: 1.2361 Epoch 449/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.2412 - mae: 1.1798 Epoch 450/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.4753 - mae: 1.2491 Epoch 451/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.1766 - mae: 1.2141 Epoch 452/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.6090 - mae: 1.1063 Epoch 453/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.6247 - mae: 1.2676 Epoch 454/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.0500 - mae: 1.1991 Epoch 455/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.2092 - mae: 1.2090 Epoch 456/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.9525 - mae: 1.1511 Epoch 457/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.8282 - mae: 1.1367 Epoch 458/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.4477 - mae: 1.2370 Epoch 459/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.1320 - mae: 1.1776 Epoch 460/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.7906 - mae: 1.1366 Epoch 461/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.3138 - mae: 1.1546 Epoch 462/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.8742 - mae: 1.1510 Epoch 463/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.8489 - mae: 1.3753 Epoch 464/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.9239 - mae: 1.3710 Epoch 465/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.8718 - mae: 1.2453 Epoch 466/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.0790 - mae: 1.2103 Epoch 467/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.1677 - mae: 1.2274 Epoch 468/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.8808 - mae: 1.1586 Epoch 469/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.0288 - mae: 1.2021 Epoch 470/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.3804 - mae: 1.2241 Epoch 471/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.9097 - mae: 1.1423 Epoch 472/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.9170 - mae: 1.1410 Epoch 473/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.1063 - mae: 1.1809 Epoch 474/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.2666 - mae: 1.1760 Epoch 475/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.6668 - mae: 1.0715 Epoch 476/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.6267 - mae: 1.0357 Epoch 477/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.4852 - mae: 1.0771 Epoch 478/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.0723 - mae: 1.1679 Epoch 479/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.3036 - mae: 1.1940 Epoch 480/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.2356 - mae: 1.1699 Epoch 481/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.4397 - mae: 1.0586 Epoch 482/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.7236 - mae: 1.0969 Epoch 483/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.6191 - mae: 1.1835 Epoch 484/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.7650 - mae: 1.1274 Epoch 485/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.5562 - mae: 1.1299 Epoch 486/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.3966 - mae: 1.2353 Epoch 487/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.7169 - mae: 1.1179 Epoch 488/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.0049 - mae: 1.1680 Epoch 489/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.7635 - mae: 1.1568 Epoch 490/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.4976 - mae: 1.2230 Epoch 491/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.8286 - mae: 1.1429 Epoch 492/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.9666 - mae: 1.1641 Epoch 493/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.9247 - mae: 1.1599 Epoch 494/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.0517 - mae: 1.1892 Epoch 495/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.8571 - mae: 1.1246 Epoch 496/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.7137 - mae: 1.0738 Epoch 497/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.6394 - mae: 1.0976 Epoch 498/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.5173 - mae: 1.0678 Epoch 499/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.4467 - mae: 1.0275 Epoch 500/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.7683 - mae: 1.1479 Model evaluation: Loss (MSE): 2.60, MAE: 1.06
models = {
"Linear Regression": LinearRegression(),
"Lasso": Lasso(),
"K-Neighbors Regressor": KNeighborsRegressor(),
"Decision Tree": DecisionTreeRegressor(),
"Random Forest Regressor": RandomForestRegressor(),
"Gradient Boosting": GradientBoostingRegressor(),
"XGBRegressor": XGBRegressor(),
"CatBoosting Regressor": CatBoostRegressor(verbose=0, iterations = 100),
"AdaBoost Regressor": AdaBoostRegressor(),
"ExtraTreesRegressor": ExtraTreesRegressor(),
"Support Vector Regressor(RBF)": SVR(kernel="rbf"),
"Support Vector Regressor(linear)": SVR(kernel="linear"),
"Nu SVR(rbf)": NuSVR(kernel="rbf"),
"ANN": ANN_model
}
def safe_flatten(y_pred):
"""
Flattens the array if it's a 2D array with shape (n, 1).
Useful for ANN predictions.
"""
if isinstance(y_pred, (np.ndarray, list)) and len(np.shape(y_pred)) == 2 and y_pred.shape[1] == 1:
return y_pred.flatten()
return y_pred
r2_train_score = {}
r2_test_score = {}
def evaluate_model(models, X_train, y_train, X_val, y_val):
for model_name, model in models.items():
model.fit(X_train, y_train)
y_train_pred = model.predict(X_train)
y_test_pred = model.predict(X_val)
y = y_val
y_pred = safe_flatten(y_test_pred)
plt.figure(figsize=(8, 6))
r2 = r2_score(y, y_pred)
sns.scatterplot(x=y, y=y_pred, label='Predicted vs Actual', color='blue')
sns.regplot(x=y, y=y_pred, scatter=False, label='Regression Line', color='red', ci=None)
plt.xlabel('Actual Values')
plt.ylabel('Predicted Values')
plt.title(f'CO2 Actual vs Predicted Values (R² Score: {r2:.4f}) for {model_name} model')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.savefig(f'CO2 Actual vs Predicted Values (R² Score: {r2:.4f}) for {model_name} model.png')
plt.show()
r2_train_score[model_name] = r2_score(y_train, y_train_pred)
r2_test_score[model_name] = r2_score(y_val, y_test_pred)
evaluate_model(models, X_train_scaled, y_train.CO2, X_val_scaled, y_val.CO2)
10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.3402 - mae: 1.0227 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step 4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step
score = pd.DataFrame(list(zip(models.keys(), r2_train_score.values(), r2_test_score.values())), columns=["Model","r2_train_score", "r2_test_score"])
score
| Model | r2_train_score | r2_test_score | |
|---|---|---|---|
| 0 | Linear Regression | 0.509402 | 0.542437 |
| 1 | Lasso | 0.452207 | 0.477392 |
| 2 | K-Neighbors Regressor | 0.838501 | 0.812381 |
| 3 | Decision Tree | 0.997671 | 0.771861 |
| 4 | Random Forest Regressor | 0.976996 | 0.876897 |
| 5 | Gradient Boosting | 0.941975 | 0.880438 |
| 6 | XGBRegressor | 0.997604 | 0.863107 |
| 7 | CatBoosting Regressor | 0.981642 | 0.885309 |
| 8 | AdaBoost Regressor | 0.833152 | 0.786018 |
| 9 | ExtraTreesRegressor | 0.997670 | 0.900201 |
| 10 | Support Vector Regressor(RBF) | 0.514539 | 0.543360 |
| 11 | Support Vector Regressor(linear) | 0.485929 | 0.502646 |
| 12 | Nu SVR(rbf) | 0.448582 | 0.462060 |
| 13 | ANN | 0.977113 | 0.871994 |
# Set positions
x = np.arange(len(score['Model']))
width = 0.35 # Width of the bars
# Create plot
fig, ax = plt.subplots(figsize=(16, 8))
bars1 = ax.bar(x - width/2, score['r2_train_score'], width, label='Train R²', color='skyblue')
bars2 = ax.bar(x + width/2, score['r2_test_score'], width, label='Validation R²', color='salmon')
# Add labels and title
ax.set_xlabel('ML Models')
ax.set_ylabel('R² Score')
ax.set_title('C02 Train vs Validation R² Score for Different Models')
ax.set_xticks(x)
ax.set_xticklabels(score['Model'], rotation=45)
ax.legend()
# Add R2 score text on top of bars
for bar in bars1 + bars2:
yval = bar.get_height()
ax.text(bar.get_x() + bar.get_width()/2.0, yval + 0.01, f'{yval:.2f}', ha='center', va='bottom')
plt.tight_layout()
plt.savefig("CO2 Train vs Validation R² Score for Different Models")
plt.show()
ANN_model = Sequential([
Dense(32, input_dim=13), # No activation here
LeakyReLU(alpha=0.1), # LeakyReLU activation
Dense(32, activation='tanh'), # Tanh for richer non-linearity
Dense(16, activation='relu'), # ReLU for simplicity
Dense(1, activation='linear') # Linear for regression output
])
# Compile the model
ANN_model.compile(optimizer='adam',
loss='mean_squared_error',
metrics=['mae'])
# Train the model
ANN_model.fit(X_train_scaled, y_train.H2, epochs=500, verbose=1)
# Evaluate the model
loss, mae = ANN_model.evaluate(X_train_scaled, y_train.H2, verbose=0)
print(f"\nModel evaluation:\nLoss (MSE): {loss:.2f}, MAE: {mae:.2f}")
Epoch 1/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 2s 35ms/step - loss: 1114.5948 - mae: 30.6551 Epoch 2/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1092.6293 - mae: 30.3200 Epoch 3/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1016.1355 - mae: 29.1121 Epoch 4/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 950.2632 - mae: 27.9327 Epoch 5/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 925.0381 - mae: 27.6578 Epoch 6/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 782.8148 - mae: 25.2815 Epoch 7/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 713.4860 - mae: 23.5926 Epoch 8/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 617.4475 - mae: 21.3423 Epoch 9/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 466.7102 - mae: 17.8205 Epoch 10/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 379.0529 - mae: 15.9844 Epoch 11/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 279.0497 - mae: 13.2619 Epoch 12/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 233.6953 - mae: 11.6012 Epoch 13/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 185.0137 - mae: 10.2930 Epoch 14/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 171.2313 - mae: 9.7690 Epoch 15/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 135.7586 - mae: 8.6040 Epoch 16/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 126.1504 - mae: 8.6905 Epoch 17/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 110.9560 - mae: 7.9227 Epoch 18/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 100.4794 - mae: 7.7887 Epoch 19/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 94.4140 - mae: 7.5600 Epoch 20/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 89.3403 - mae: 7.3504 Epoch 21/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 84.1876 - mae: 7.1113 Epoch 22/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 77.9369 - mae: 6.9014 Epoch 23/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 70.5678 - mae: 6.6286 Epoch 24/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 70.5308 - mae: 6.5333 Epoch 25/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 68.7351 - mae: 6.5970 Epoch 26/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 66.9746 - mae: 6.4009 Epoch 27/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 59.5299 - mae: 6.0411 Epoch 28/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 63.8065 - mae: 6.2055 Epoch 29/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 64.7406 - mae: 6.3353 Epoch 30/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 58.8110 - mae: 6.0271 Epoch 31/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 56.1725 - mae: 5.9506 Epoch 32/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 51.9402 - mae: 5.6167 Epoch 33/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 51.1221 - mae: 5.6004 Epoch 34/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 56.9818 - mae: 5.9647 Epoch 35/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 53.5437 - mae: 5.8061 Epoch 36/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 45.4872 - mae: 5.2557 Epoch 37/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 50.6648 - mae: 5.5402 Epoch 38/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 50.1591 - mae: 5.5287 Epoch 39/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 49.0068 - mae: 5.4025 Epoch 40/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 40.5049 - mae: 4.8911 Epoch 41/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 47.4356 - mae: 5.3942 Epoch 42/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 39.6630 - mae: 4.9123 Epoch 43/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 38.3502 - mae: 4.8363 Epoch 44/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 42.1500 - mae: 4.9903 Epoch 45/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 35.0358 - mae: 4.6555 Epoch 46/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 35.1659 - mae: 4.7034 Epoch 47/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 38.4428 - mae: 4.7524 Epoch 48/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 34.3109 - mae: 4.4980 Epoch 49/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 37.6402 - mae: 4.6525 Epoch 50/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 39.7448 - mae: 4.8190 Epoch 51/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 32.8882 - mae: 4.2895 Epoch 52/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 31.5809 - mae: 4.3003 Epoch 53/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 30.1885 - mae: 4.1537 Epoch 54/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 34.3234 - mae: 4.3767 Epoch 55/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 32.0350 - mae: 4.1690 Epoch 56/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 34.1826 - mae: 4.2859 Epoch 57/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 28.0954 - mae: 3.9268 Epoch 58/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 29.0354 - mae: 4.0484 Epoch 59/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 28.5883 - mae: 3.9239 Epoch 60/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 25.4623 - mae: 3.7134 Epoch 61/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 29.6323 - mae: 4.0048 Epoch 62/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 27.1073 - mae: 3.8058 Epoch 63/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 25.8526 - mae: 3.5992 Epoch 64/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 22.4837 - mae: 3.5261 Epoch 65/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 24.3290 - mae: 3.5894 Epoch 66/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 22.3423 - mae: 3.4705 Epoch 67/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 24.8294 - mae: 3.6171 Epoch 68/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 22.2324 - mae: 3.4568 Epoch 69/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 21.4214 - mae: 3.3360 Epoch 70/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 23.1652 - mae: 3.4849 Epoch 71/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 21.0867 - mae: 3.3631 Epoch 72/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 21.4526 - mae: 3.2845 Epoch 73/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 20.9731 - mae: 3.3159 Epoch 74/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 18.9955 - mae: 3.1295 Epoch 75/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 19.0359 - mae: 3.1859 Epoch 76/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 20.7746 - mae: 3.3432 Epoch 77/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 19.9059 - mae: 3.1181 Epoch 78/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 23.8073 - mae: 3.4327 Epoch 79/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 19.2646 - mae: 3.1973 Epoch 80/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 17.6540 - mae: 3.0792 Epoch 81/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 17.8386 - mae: 3.1069 Epoch 82/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 17.4348 - mae: 2.9877 Epoch 83/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 18.3110 - mae: 2.9578 Epoch 84/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 16.7106 - mae: 2.8899 Epoch 85/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 19.1399 - mae: 3.1721 Epoch 86/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 18.6142 - mae: 3.1050 Epoch 87/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 16.1353 - mae: 2.8624 Epoch 88/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 17.5072 - mae: 2.9965 Epoch 89/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 18.2345 - mae: 2.9513 Epoch 90/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 15.4015 - mae: 2.8102 Epoch 91/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 15.4113 - mae: 2.8092 Epoch 92/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 17.2828 - mae: 2.9984 Epoch 93/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.9517 - mae: 2.5741 Epoch 94/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 15.4109 - mae: 2.7929 Epoch 95/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 15.1049 - mae: 2.7245 Epoch 96/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 16.1652 - mae: 2.8040 Epoch 97/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.9413 - mae: 2.5375 Epoch 98/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.2699 - mae: 2.4382 Epoch 99/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 16.3297 - mae: 2.6747 Epoch 100/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 13.9504 - mae: 2.6735 Epoch 101/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.0888 - mae: 2.6060 Epoch 102/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 17.1756 - mae: 2.8884 Epoch 103/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.7709 - mae: 2.5452 Epoch 104/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.8165 - mae: 2.5843 Epoch 105/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.8161 - mae: 2.4115 Epoch 106/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 13.2415 - mae: 2.5811 Epoch 107/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.4562 - mae: 2.3810 Epoch 108/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.0395 - mae: 2.5435 Epoch 109/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.2404 - mae: 2.6316 Epoch 110/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.0284 - mae: 2.4667 Epoch 111/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.0550 - mae: 2.5302 Epoch 112/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.7273 - mae: 2.2651 Epoch 113/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.3233 - mae: 2.3046 Epoch 114/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.1697 - mae: 2.5089 Epoch 115/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.9296 - mae: 2.3336 Epoch 116/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.5377 - mae: 2.3086 Epoch 117/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.8539 - mae: 2.3688 Epoch 118/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.2646 - mae: 2.3152 Epoch 119/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.2454 - mae: 2.2108 Epoch 120/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.7935 - mae: 2.4377 Epoch 121/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.8773 - mae: 2.3480 Epoch 122/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.5365 - mae: 2.3916 Epoch 123/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.1099 - mae: 2.2963 Epoch 124/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.0959 - mae: 2.1998 Epoch 125/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.5071 - mae: 2.5400 Epoch 126/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.4520 - mae: 2.2113 Epoch 127/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 14.5461 - mae: 2.5842 Epoch 128/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.7880 - mae: 2.1922 Epoch 129/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.1188 - mae: 2.2976 Epoch 130/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.5429 - mae: 2.1590 Epoch 131/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.5511 - mae: 2.2351 Epoch 132/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.1897 - mae: 2.2316 Epoch 133/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.6210 - mae: 2.2059 Epoch 134/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.8312 - mae: 2.3434 Epoch 135/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.5395 - mae: 2.0961 Epoch 136/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.9462 - mae: 2.2693 Epoch 137/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.3222 - mae: 2.2488 Epoch 138/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.8143 - mae: 2.0755 Epoch 139/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.3606 - mae: 2.2161 Epoch 140/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.6472 - mae: 2.2352 Epoch 141/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.0597 - mae: 2.1483 Epoch 142/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.7616 - mae: 2.0117 Epoch 143/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.9447 - mae: 2.2432 Epoch 144/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.2080 - mae: 2.2716 Epoch 145/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.5173 - mae: 2.0969 Epoch 146/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.8170 - mae: 2.1970 Epoch 147/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.6614 - mae: 2.0495 Epoch 148/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.8627 - mae: 2.1863 Epoch 149/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.6663 - mae: 2.0223 Epoch 150/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.3985 - mae: 2.1448 Epoch 151/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.1086 - mae: 1.8983 Epoch 152/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.5836 - mae: 2.2107 Epoch 153/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.7922 - mae: 2.1377 Epoch 154/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.6874 - mae: 2.0690 Epoch 155/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.1593 - mae: 2.0107 Epoch 156/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.0320 - mae: 1.9707 Epoch 157/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.3945 - mae: 2.1413 Epoch 158/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.8443 - mae: 2.1230 Epoch 159/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.6169 - mae: 1.9797 Epoch 160/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.5259 - mae: 2.0268 Epoch 161/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.4618 - mae: 2.0676 Epoch 162/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.5185 - mae: 2.1594 Epoch 163/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.6301 - mae: 2.1115 Epoch 164/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.0644 - mae: 2.0734 Epoch 165/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.6315 - mae: 2.0954 Epoch 166/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.4702 - mae: 1.8991 Epoch 167/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.9594 - mae: 2.2178 Epoch 168/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.3868 - mae: 2.0571 Epoch 169/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.3466 - mae: 2.1073 Epoch 170/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.1306 - mae: 1.8311 Epoch 171/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.4727 - mae: 2.0563 Epoch 172/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.3707 - mae: 1.9968 Epoch 173/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.5698 - mae: 1.8581 Epoch 174/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.0879 - mae: 1.9908 Epoch 175/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.5890 - mae: 1.9101 Epoch 176/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.0711 - mae: 2.0736 Epoch 177/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.1819 - mae: 1.9321 Epoch 178/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.7444 - mae: 1.8828 Epoch 179/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.8824 - mae: 1.8847 Epoch 180/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.1463 - mae: 1.9676 Epoch 181/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.6734 - mae: 1.9029 Epoch 182/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.2891 - mae: 2.0640 Epoch 183/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.0317 - mae: 1.9968 Epoch 184/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.5230 - mae: 1.7912 Epoch 185/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.7749 - mae: 2.1145 Epoch 186/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.9760 - mae: 1.9564 Epoch 187/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.2445 - mae: 1.8696 Epoch 188/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.7488 - mae: 2.0677 Epoch 189/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.8752 - mae: 2.1372 Epoch 190/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 11.0457 - mae: 2.1318 Epoch 191/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.7947 - mae: 1.9966 Epoch 192/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.4591 - mae: 1.8224 Epoch 193/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.3937 - mae: 1.8791 Epoch 194/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.3974 - mae: 1.9366 Epoch 195/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.8741 - mae: 1.9335 Epoch 196/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.4772 - mae: 1.7487 Epoch 197/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.8496 - mae: 1.8726 Epoch 198/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.9928 - mae: 1.8353 Epoch 199/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.2892 - mae: 1.7971 Epoch 200/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.5112 - 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loss: 10.3973 - mae: 1.9402 Epoch 214/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.6419 - mae: 1.7870 Epoch 215/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.5429 - mae: 1.8089 Epoch 216/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.4012 - mae: 1.7884 Epoch 217/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.8683 - mae: 1.8049 Epoch 218/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.3755 - mae: 1.9024 Epoch 219/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.7541 - mae: 1.7697 Epoch 220/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.5222 - mae: 1.9829 Epoch 221/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.4539 - mae: 1.6469 Epoch 222/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.8794 - mae: 1.8283 Epoch 223/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.5230 - mae: 1.8031 Epoch 224/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.6973 - mae: 1.7187 Epoch 225/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.2125 - 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loss: 11.8370 - mae: 2.2791 Epoch 239/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.3312 - mae: 2.0411 Epoch 240/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.9957 - mae: 1.7641 Epoch 241/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.6234 - mae: 1.7176 Epoch 242/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.8208 - mae: 1.8211 Epoch 243/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.1348 - mae: 1.6715 Epoch 244/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.4715 - mae: 1.5996 Epoch 245/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.3844 - mae: 1.5969 Epoch 246/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.4936 - mae: 1.5854 Epoch 247/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.1469 - mae: 1.7919 Epoch 248/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.1041 - mae: 1.9110 Epoch 249/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.4767 - mae: 1.7406 Epoch 250/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.9387 - mae: 1.7127 Epoch 251/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.9079 - mae: 1.8278 Epoch 252/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.0146 - mae: 1.6697 Epoch 253/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.3141 - mae: 1.5889 Epoch 254/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.9803 - mae: 1.6378 Epoch 255/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.1679 - mae: 1.6577 Epoch 256/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.1172 - mae: 1.8403 Epoch 257/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.7389 - mae: 1.7300 Epoch 258/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.9727 - mae: 1.5645 Epoch 259/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.1053 - mae: 1.7054 Epoch 260/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.7324 - mae: 1.5636 Epoch 261/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.7702 - mae: 1.6606 Epoch 262/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.1102 - mae: 1.5558 Epoch 263/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.2187 - mae: 1.7529 Epoch 264/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.6487 - mae: 1.5869 Epoch 265/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.0549 - mae: 1.6875 Epoch 266/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.7540 - mae: 1.6745 Epoch 267/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.4513 - mae: 1.5995 Epoch 268/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.9412 - mae: 1.4961 Epoch 269/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.8305 - mae: 1.5548 Epoch 270/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.1986 - mae: 1.5340 Epoch 271/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.2205 - mae: 1.6794 Epoch 272/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.1818 - mae: 1.7159 Epoch 273/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.7046 - mae: 1.6881 Epoch 274/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.1028 - mae: 1.6782 Epoch 275/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.2425 - mae: 1.5443 Epoch 276/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.9231 - mae: 1.7119 Epoch 277/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.4070 - mae: 1.6104 Epoch 278/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.1086 - mae: 1.5280 Epoch 279/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.2192 - mae: 1.5742 Epoch 280/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.3833 - mae: 1.5374 Epoch 281/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.3732 - mae: 1.5216 Epoch 282/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.2961 - mae: 1.7057 Epoch 283/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.7185 - mae: 1.5384 Epoch 284/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.4435 - mae: 1.5106 Epoch 285/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.6901 - mae: 1.4351 Epoch 286/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.6154 - mae: 1.5610 Epoch 287/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.0514 - mae: 1.6623 Epoch 288/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.6747 - mae: 1.5369 Epoch 289/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - 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loss: 6.5329 - mae: 1.5103 Epoch 342/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.9771 - mae: 1.4264 Epoch 343/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.9666 - mae: 1.4862 Epoch 344/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.9080 - mae: 1.5821 Epoch 345/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.7900 - mae: 1.4248 Epoch 346/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.7387 - mae: 1.3954 Epoch 347/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.0994 - mae: 1.3406 Epoch 348/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.3751 - mae: 1.3677 Epoch 349/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.8856 - mae: 1.5536 Epoch 350/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.1493 - mae: 1.6020 Epoch 351/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.0260 - mae: 1.4050 Epoch 352/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.3759 - mae: 1.4909 Epoch 353/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.1827 - mae: 1.5589 Epoch 354/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - 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loss: 3.9827 - mae: 1.2888 Epoch 446/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.5678 - mae: 1.3328 Epoch 447/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.8111 - mae: 1.2426 Epoch 448/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.6780 - mae: 1.4879 Epoch 449/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.4567 - mae: 1.2718 Epoch 450/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.9940 - mae: 1.3376 Epoch 451/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.5870 - mae: 1.2043 Epoch 452/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.8427 - mae: 1.3439 Epoch 453/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.7806 - mae: 1.3244 Epoch 454/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.1371 - mae: 1.3259 Epoch 455/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.7375 - mae: 1.2873 Epoch 456/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.7486 - mae: 1.2529 Epoch 457/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.0368 - mae: 1.1581 Epoch 458/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.1106 - mae: 1.1513 Epoch 459/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.3723 - mae: 1.3469 Epoch 460/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.6502 - mae: 1.4341 Epoch 461/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.5956 - mae: 1.2854 Epoch 462/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.0966 - mae: 1.3140 Epoch 463/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.9249 - mae: 1.2941 Epoch 464/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.1178 - mae: 1.2350 Epoch 465/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.1492 - mae: 1.3006 Epoch 466/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.5485 - mae: 1.3094 Epoch 467/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.7805 - mae: 1.3461 Epoch 468/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.3751 - mae: 1.3163 Epoch 469/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.2850 - mae: 1.2175 Epoch 470/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.9013 - mae: 1.2812 Epoch 471/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.8036 - mae: 1.3916 Epoch 472/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.9086 - mae: 1.3535 Epoch 473/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.6836 - mae: 1.4228 Epoch 474/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.2234 - mae: 1.3286 Epoch 475/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.2839 - mae: 1.2185 Epoch 476/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.2718 - mae: 1.2781 Epoch 477/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.2997 - mae: 1.2415 Epoch 478/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.3531 - mae: 1.2744 Epoch 479/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.9929 - mae: 1.3104 Epoch 480/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.4795 - mae: 1.3177 Epoch 481/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.2462 - mae: 1.3406 Epoch 482/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.0282 - mae: 1.3370 Epoch 483/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.7476 - mae: 1.2154 Epoch 484/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.8581 - mae: 1.2283 Epoch 485/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.9911 - mae: 1.2803 Epoch 486/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.3683 - mae: 1.2385 Epoch 487/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.6179 - mae: 1.2705 Epoch 488/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.5909 - mae: 1.3205 Epoch 489/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.2040 - mae: 1.1651 Epoch 490/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.6336 - mae: 1.2532 Epoch 491/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.5880 - mae: 1.2729 Epoch 492/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.1006 - mae: 1.2435 Epoch 493/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.5694 - mae: 1.1555 Epoch 494/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.1799 - mae: 1.2588 Epoch 495/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.7182 - mae: 1.2703 Epoch 496/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.7736 - mae: 1.2977 Epoch 497/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.7881 - mae: 1.2506 Epoch 498/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.7631 - mae: 1.2596 Epoch 499/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.2382 - mae: 1.1648 Epoch 500/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.4652 - mae: 1.2477 Model evaluation: Loss (MSE): 4.13, MAE: 1.23
models = {
"Linear Regression": LinearRegression(),
"Lasso": Lasso(),
"K-Neighbors Regressor": KNeighborsRegressor(),
"Decision Tree": DecisionTreeRegressor(),
"Random Forest Regressor": RandomForestRegressor(),
"Gradient Boosting": GradientBoostingRegressor(),
"XGBRegressor": XGBRegressor(),
"CatBoosting Regressor": CatBoostRegressor(verbose=0, iterations = 100),
"AdaBoost Regressor": AdaBoostRegressor(),
"ExtraTreesRegressor": ExtraTreesRegressor(),
"Support Vector Regressor(RBF)": SVR(kernel="rbf"),
"Support Vector Regressor(linear)": SVR(kernel="linear"),
"Nu SVR(rbf)": NuSVR(kernel="rbf"),
"ANN": ANN_model
}
def safe_flatten(y_pred):
"""
Flattens the array if it's a 2D array with shape (n, 1).
Useful for ANN predictions.
"""
if isinstance(y_pred, (np.ndarray, list)) and len(np.shape(y_pred)) == 2 and y_pred.shape[1] == 1:
return y_pred.flatten()
return y_pred
r2_train_score = {}
r2_test_score = {}
def evaluate_model(models, X_train, y_train, X_val, y_val):
for model_name, model in models.items():
model.fit(X_train, y_train)
y_train_pred = model.predict(X_train)
y_test_pred = model.predict(X_val)
y = y_val
y_pred = safe_flatten(y_test_pred)
plt.figure(figsize=(8, 6))
r2 = r2_score(y, y_pred)
sns.scatterplot(x=y, y=y_pred, label='Predicted vs Actual', color='blue')
sns.regplot(x=y, y=y_pred, scatter=False, label='Regression Line', color='red', ci=None)
plt.xlabel('Actual Values')
plt.ylabel('Predicted Values')
plt.title(f'H2 Actual vs Predicted Values (R² Score: {r2:.4f}) for {model_name} model')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.savefig(f'H2 Actual vs Predicted Values (R² Score: {r2:.4f}) for {model_name} model.png')
plt.show()
r2_train_score[model_name] = r2_score(y_train, y_train_pred)
r2_test_score[model_name] = r2_score(y_val, y_test_pred)
evaluate_model(models, X_train_scaled, y_train.H2, X_val_scaled, y_val.H2)
10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.4085 - mae: 1.2107 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step 4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
score = pd.DataFrame(list(zip(models.keys(), r2_train_score.values(), r2_test_score.values())), columns=["Model","r2_train_score", "r2_test_score"])
score
| Model | r2_train_score | r2_test_score | |
|---|---|---|---|
| 0 | Linear Regression | 0.701773 | 0.730645 |
| 1 | Lasso | 0.650434 | 0.684888 |
| 2 | K-Neighbors Regressor | 0.896657 | 0.911715 |
| 3 | Decision Tree | 0.998124 | 0.914972 |
| 4 | Random Forest Regressor | 0.983950 | 0.942061 |
| 5 | Gradient Boosting | 0.966199 | 0.950161 |
| 6 | XGBRegressor | 0.998037 | 0.932889 |
| 7 | CatBoosting Regressor | 0.986135 | 0.951031 |
| 8 | AdaBoost Regressor | 0.856201 | 0.868602 |
| 9 | ExtraTreesRegressor | 0.998123 | 0.939666 |
| 10 | Support Vector Regressor(RBF) | 0.568170 | 0.564914 |
| 11 | Support Vector Regressor(linear) | 0.670485 | 0.721979 |
| 12 | Nu SVR(rbf) | 0.580037 | 0.584159 |
| 13 | ANN | 0.975372 | 0.945724 |
# Set positions
x = np.arange(len(score['Model']))
width = 0.35 # Width of the bars
# Create plot
fig, ax = plt.subplots(figsize=(16, 8))
bars1 = ax.bar(x - width/2, score['r2_train_score'], width, label='Train R²', color='skyblue')
bars2 = ax.bar(x + width/2, score['r2_test_score'], width, label='Validation R²', color='salmon')
# Add labels and title
ax.set_xlabel('ML Models')
ax.set_ylabel('R² Score')
ax.set_title('H2 Train vs Validation R² Score for Different Models')
ax.set_xticks(x)
ax.set_xticklabels(score['Model'], rotation=45)
ax.legend()
# Add R2 score text on top of bars
for bar in bars1 + bars2:
yval = bar.get_height()
ax.text(bar.get_x() + bar.get_width()/2.0, yval + 0.01, f'{yval:.2f}', ha='center', va='bottom')
plt.tight_layout()
plt.savefig("H2 Train vs Validation R² Score for Different Models")
plt.show()
ANN_model = Sequential([
Dense(32, input_dim=13), # No activation here
LeakyReLU(alpha=0.1), # LeakyReLU activation
Dense(32, activation='tanh'), # Tanh for richer non-linearity
Dense(16, activation='relu'), # ReLU for simplicity
Dense(1, activation='linear') # Linear for regression output
])
# Compile the model
ANN_model.compile(optimizer='adam',
loss='mean_squared_error',
metrics=['mae'])
# Train the model
ANN_model.fit(X_train_scaled, y_train.CH4, epochs=500, verbose=1)
# Evaluate the model
loss, mae = ANN_model.evaluate(X_train_scaled, y_train.CH4, verbose=0)
print(f"\nModel evaluation:\nLoss (MSE): {loss:.2f}, MAE: {mae:.2f}")
Epoch 1/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 2s 33ms/step - loss: 70.6160 - mae: 7.6613 Epoch 2/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 67.3477 - mae: 7.4664 Epoch 3/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 64.4833 - mae: 7.1564 Epoch 4/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 53.4934 - mae: 6.4522 Epoch 5/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 42.2167 - mae: 5.4767 Epoch 6/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 31.6310 - mae: 4.5404 Epoch 7/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 23.5081 - mae: 3.8119 Epoch 8/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 16.0438 - mae: 3.1950 Epoch 9/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.8049 - mae: 2.8408 Epoch 10/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.6834 - mae: 2.8292 Epoch 11/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 12.3268 - mae: 2.7783 Epoch 12/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.5295 - mae: 2.5724 Epoch 13/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.8168 - mae: 2.6067 Epoch 14/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.1576 - mae: 2.5383 Epoch 15/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.8011 - mae: 2.3645 Epoch 16/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 10.1528 - mae: 2.4611 Epoch 17/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.6955 - mae: 2.4197 Epoch 18/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.7866 - mae: 2.2507 Epoch 19/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 9.3735 - mae: 2.3614 Epoch 20/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.3820 - mae: 2.2502 Epoch 21/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.3800 - mae: 2.2129 Epoch 22/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.9988 - mae: 2.1659 Epoch 23/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.4071 - mae: 2.1871 Epoch 24/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.6412 - mae: 2.2654 Epoch 25/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.0446 - mae: 1.9809 Epoch 26/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.4494 - mae: 2.0710 Epoch 27/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 8.0073 - mae: 2.2071 Epoch 28/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.6046 - mae: 2.1474 Epoch 29/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.3849 - mae: 1.9433 Epoch 30/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.7095 - mae: 2.1149 Epoch 31/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 7.1924 - mae: 2.0663 Epoch 32/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.3011 - mae: 1.7482 Epoch 33/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.4545 - mae: 1.9535 Epoch 34/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.6591 - mae: 1.8567 Epoch 35/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 6.1660 - mae: 1.8697 Epoch 36/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.4235 - mae: 1.7936 Epoch 37/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.3126 - mae: 1.7244 Epoch 38/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.3850 - mae: 1.7079 Epoch 39/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.7843 - mae: 1.7979 Epoch 40/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.6026 - mae: 1.8008 Epoch 41/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.2041 - mae: 1.7255 Epoch 42/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.6470 - mae: 1.7086 Epoch 43/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.9662 - mae: 1.7107 Epoch 44/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.5097 - mae: 1.6028 Epoch 45/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.2323 - mae: 1.6789 Epoch 46/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.8807 - mae: 1.6514 Epoch 47/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.1146 - mae: 1.6787 Epoch 48/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.1518 - mae: 1.5293 Epoch 49/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 5.1279 - mae: 1.6591 Epoch 50/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.5148 - mae: 1.5401 Epoch 51/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.8827 - mae: 1.4627 Epoch 52/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.5812 - mae: 1.5410 Epoch 53/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.7875 - mae: 1.6548 Epoch 54/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.0076 - mae: 1.4573 Epoch 55/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.2892 - mae: 1.3714 Epoch 56/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.4445 - mae: 1.5346 Epoch 57/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.3629 - mae: 1.4796 Epoch 58/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.1060 - mae: 1.4810 Epoch 59/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.5897 - mae: 1.4172 Epoch 60/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.2000 - mae: 1.4724 Epoch 61/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.2431 - mae: 1.5309 Epoch 62/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.5536 - mae: 1.4022 Epoch 63/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.3941 - mae: 1.3886 Epoch 64/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.2493 - mae: 1.2816 Epoch 65/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.7941 - mae: 1.4107 Epoch 66/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.4055 - mae: 1.4253 Epoch 67/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.4501 - mae: 1.3525 Epoch 68/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.8482 - mae: 1.2273 Epoch 69/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.0341 - mae: 1.2827 Epoch 70/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.2336 - mae: 1.2588 Epoch 71/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.9249 - mae: 1.2445 Epoch 72/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.3735 - mae: 1.2980 Epoch 73/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.4526 - mae: 1.3387 Epoch 74/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.8986 - mae: 1.2358 Epoch 75/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.5092 - mae: 1.3499 Epoch 76/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.9582 - mae: 1.3936 Epoch 77/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.2539 - mae: 1.3001 Epoch 78/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.6417 - mae: 1.3712 Epoch 79/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.9017 - mae: 1.2169 Epoch 80/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.6269 - mae: 1.1965 Epoch 81/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.0454 - mae: 1.3247 Epoch 82/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.7071 - mae: 1.1964 Epoch 83/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.7534 - mae: 1.2072 Epoch 84/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.2172 - mae: 1.2585 Epoch 85/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.8604 - mae: 1.2009 Epoch 86/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.3693 - mae: 1.1330 Epoch 87/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.4616 - mae: 1.3586 Epoch 88/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.7562 - mae: 1.1518 Epoch 89/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.6645 - mae: 1.1766 Epoch 90/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.1838 - mae: 1.0551 Epoch 91/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.6251 - mae: 1.1505 Epoch 92/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.0481 - mae: 1.0387 Epoch 93/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 3.0150 - mae: 1.1822 Epoch 94/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.3989 - mae: 1.0590 Epoch 95/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.4187 - mae: 1.1082 Epoch 96/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.4059 - mae: 1.1114 Epoch 97/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.7873 - mae: 1.1642 Epoch 98/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.1881 - mae: 1.0860 Epoch 99/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.6421 - mae: 1.1545 Epoch 100/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.5668 - mae: 1.1148 Epoch 101/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.3790 - mae: 1.1245 Epoch 102/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.6308 - mae: 1.1344 Epoch 103/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.9022 - mae: 0.9783 Epoch 104/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.0725 - mae: 1.0139 Epoch 105/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.1113 - mae: 1.0321 Epoch 106/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.2606 - mae: 1.0680 Epoch 107/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.3831 - mae: 1.0577 Epoch 108/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.8270 - mae: 0.9506 Epoch 109/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.3821 - mae: 1.0660 Epoch 110/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.8893 - mae: 0.9990 Epoch 111/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.4212 - mae: 1.0995 Epoch 112/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.2983 - mae: 1.0525 Epoch 113/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.7501 - mae: 0.9437 Epoch 114/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.2655 - mae: 1.0439 Epoch 115/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.8413 - mae: 0.9441 Epoch 116/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.5750 - mae: 0.9140 Epoch 117/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.8818 - mae: 0.9506 Epoch 118/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.9090 - mae: 0.9527 Epoch 119/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.7036 - mae: 0.9043 Epoch 120/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.8442 - mae: 0.9417 Epoch 121/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.9014 - mae: 0.9426 Epoch 122/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.9455 - mae: 0.9591 Epoch 123/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.9879 - mae: 0.9574 Epoch 124/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.6484 - mae: 0.9014 Epoch 125/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.6820 - mae: 0.9016 Epoch 126/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.9723 - mae: 0.9020 Epoch 127/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.7140 - mae: 0.9240 Epoch 128/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.9237 - mae: 0.9434 Epoch 129/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.6188 - mae: 0.8930 Epoch 130/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.8393 - mae: 0.9466 Epoch 131/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.1072 - mae: 0.9716 Epoch 132/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.1454 - mae: 0.9995 Epoch 133/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.6137 - mae: 0.9135 Epoch 134/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.9466 - mae: 0.9061 Epoch 135/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.9614 - mae: 0.9459 Epoch 136/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.0597 - mae: 0.9739 Epoch 137/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.7268 - mae: 0.9127 Epoch 138/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.8693 - mae: 0.9604 Epoch 139/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.5961 - mae: 0.8997 Epoch 140/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.9315 - mae: 0.9334 Epoch 141/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.8167 - mae: 0.9400 Epoch 142/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.5974 - mae: 0.8776 Epoch 143/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.6477 - mae: 0.8815 Epoch 144/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.8099 - mae: 0.9410 Epoch 145/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3409 - mae: 0.8179 Epoch 146/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 2.0140 - mae: 0.9336 Epoch 147/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.5765 - mae: 0.8702 Epoch 148/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.4413 - mae: 0.8122 Epoch 149/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.5498 - mae: 0.8641 Epoch 150/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.6094 - mae: 0.9048 Epoch 151/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.5682 - mae: 0.8713 Epoch 152/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.7679 - mae: 0.8995 Epoch 153/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.6262 - mae: 0.8826 Epoch 154/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2974 - mae: 0.7609 Epoch 155/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3416 - mae: 0.8277 Epoch 156/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3980 - mae: 0.8240 Epoch 157/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.7966 - mae: 0.9172 Epoch 158/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.6192 - mae: 0.8875 Epoch 159/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.6593 - mae: 0.8809 Epoch 160/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.4676 - mae: 0.8496 Epoch 161/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.4575 - mae: 0.8376 Epoch 162/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.4609 - mae: 0.8701 Epoch 163/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.4645 - mae: 0.7702 Epoch 164/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2141 - mae: 0.7704 Epoch 165/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.6663 - mae: 0.8837 Epoch 166/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3792 - mae: 0.8075 Epoch 167/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3996 - mae: 0.8480 Epoch 168/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2760 - mae: 0.8140 Epoch 169/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.9824 - mae: 0.9692 Epoch 170/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.4997 - mae: 0.8673 Epoch 171/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.5659 - mae: 0.8470 Epoch 172/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.4187 - mae: 0.8239 Epoch 173/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2377 - mae: 0.7494 Epoch 174/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.6629 - mae: 0.8757 Epoch 175/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.5143 - mae: 0.8550 Epoch 176/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.4326 - mae: 0.8327 Epoch 177/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.5989 - mae: 0.8583 Epoch 178/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.5699 - mae: 0.8404 Epoch 179/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.5473 - mae: 0.8285 Epoch 180/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3454 - mae: 0.8266 Epoch 181/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3735 - mae: 0.8323 Epoch 182/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1374 - mae: 0.7315 Epoch 183/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3483 - mae: 0.7927 Epoch 184/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1501 - mae: 0.7668 Epoch 185/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.6541 - mae: 0.8575 Epoch 186/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.5430 - mae: 0.8465 Epoch 187/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.5626 - mae: 0.8491 Epoch 188/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.4042 - mae: 0.8058 Epoch 189/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.6736 - mae: 0.8591 Epoch 190/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2800 - mae: 0.7956 Epoch 191/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.4494 - mae: 0.8154 Epoch 192/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2680 - mae: 0.7744 Epoch 193/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.4634 - mae: 0.7994 Epoch 194/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2423 - mae: 0.7584 Epoch 195/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.5262 - mae: 0.8349 Epoch 196/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3783 - mae: 0.8160 Epoch 197/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3314 - mae: 0.7922 Epoch 198/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.5603 - mae: 0.8174 Epoch 199/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3356 - mae: 0.7514 Epoch 200/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2577 - mae: 0.7678 Epoch 201/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3354 - mae: 0.8149 Epoch 202/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3238 - mae: 0.7848 Epoch 203/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3904 - mae: 0.8046 Epoch 204/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0952 - mae: 0.7654 Epoch 205/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1305 - mae: 0.7352 Epoch 206/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1891 - mae: 0.7615 Epoch 207/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.4054 - mae: 0.8069 Epoch 208/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2488 - mae: 0.7582 Epoch 209/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.4230 - mae: 0.8258 Epoch 210/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2159 - mae: 0.7742 Epoch 211/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1850 - mae: 0.7596 Epoch 212/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1784 - mae: 0.7351 Epoch 213/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0477 - mae: 0.7052 Epoch 214/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.5931 - mae: 0.8128 Epoch 215/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2815 - mae: 0.7677 Epoch 216/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2788 - mae: 0.7630 Epoch 217/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9537 - mae: 0.6925 Epoch 218/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0886 - mae: 0.7243 Epoch 219/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3045 - mae: 0.7542 Epoch 220/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0693 - mae: 0.7230 Epoch 221/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.5097 - mae: 0.8097 Epoch 222/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1612 - mae: 0.7460 Epoch 223/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9942 - mae: 0.6677 Epoch 224/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1223 - mae: 0.7157 Epoch 225/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1008 - mae: 0.7213 Epoch 226/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.4091 - mae: 0.7587 Epoch 227/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0882 - mae: 0.7121 Epoch 228/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9510 - mae: 0.6831 Epoch 229/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2222 - mae: 0.7332 Epoch 230/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.4082 - mae: 0.7727 Epoch 231/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3565 - mae: 0.7392 Epoch 232/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9561 - mae: 0.6831 Epoch 233/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2139 - mae: 0.7154 Epoch 234/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0600 - mae: 0.7202 Epoch 235/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0548 - mae: 0.6985 Epoch 236/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1280 - mae: 0.7217 Epoch 237/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1638 - mae: 0.7333 Epoch 238/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9602 - mae: 0.6779 Epoch 239/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1121 - mae: 0.7170 Epoch 240/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1837 - mae: 0.7256 Epoch 241/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0395 - mae: 0.7047 Epoch 242/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2669 - mae: 0.7388 Epoch 243/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1946 - mae: 0.7203 Epoch 244/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1240 - mae: 0.7401 Epoch 245/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1183 - mae: 0.7217 Epoch 246/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2161 - mae: 0.7351 Epoch 247/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1895 - mae: 0.7459 Epoch 248/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3629 - mae: 0.7579 Epoch 249/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0308 - mae: 0.6975 Epoch 250/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2433 - mae: 0.7711 Epoch 251/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0634 - mae: 0.6841 Epoch 252/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1000 - mae: 0.7133 Epoch 253/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3446 - mae: 0.7421 Epoch 254/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2156 - mae: 0.7310 Epoch 255/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0830 - mae: 0.7280 Epoch 256/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2456 - mae: 0.7582 Epoch 257/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9643 - mae: 0.6617 Epoch 258/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3321 - mae: 0.7617 Epoch 259/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2529 - mae: 0.7236 Epoch 260/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9092 - mae: 0.6477 Epoch 261/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1769 - mae: 0.7254 Epoch 262/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3940 - mae: 0.7260 Epoch 263/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2523 - mae: 0.7200 Epoch 264/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3162 - mae: 0.7442 Epoch 265/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2829 - mae: 0.7513 Epoch 266/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0449 - mae: 0.7030 Epoch 267/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9424 - mae: 0.6881 Epoch 268/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3093 - mae: 0.7178 Epoch 269/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0206 - mae: 0.6624 Epoch 270/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9399 - mae: 0.6543 Epoch 271/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3514 - mae: 0.7346 Epoch 272/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2914 - mae: 0.7249 Epoch 273/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1557 - mae: 0.7589 Epoch 274/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2652 - mae: 0.7585 Epoch 275/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0496 - mae: 0.6791 Epoch 276/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.4373 - mae: 0.7408 Epoch 277/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1234 - mae: 0.7037 Epoch 278/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0827 - mae: 0.7222 Epoch 279/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0610 - mae: 0.7003 Epoch 280/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9371 - mae: 0.6900 Epoch 281/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0118 - mae: 0.6881 Epoch 282/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9556 - mae: 0.6381 Epoch 283/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1929 - mae: 0.7074 Epoch 284/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0423 - mae: 0.7081 Epoch 285/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9400 - mae: 0.6582 Epoch 286/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0895 - mae: 0.6892 Epoch 287/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0601 - mae: 0.6677 Epoch 288/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0374 - mae: 0.6920 Epoch 289/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9185 - mae: 0.6640 Epoch 290/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9431 - mae: 0.6692 Epoch 291/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9391 - mae: 0.6216 Epoch 292/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9167 - mae: 0.6531 Epoch 293/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0084 - mae: 0.6657 Epoch 294/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0180 - mae: 0.6322 Epoch 295/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8524 - mae: 0.6094 Epoch 296/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9020 - mae: 0.6025 Epoch 297/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1155 - mae: 0.7014 Epoch 298/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0438 - mae: 0.6529 Epoch 299/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9610 - mae: 0.6119 Epoch 300/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2405 - mae: 0.7017 Epoch 301/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8934 - mae: 0.6289 Epoch 302/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0901 - mae: 0.6500 Epoch 303/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0268 - mae: 0.6503 Epoch 304/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0612 - mae: 0.6857 Epoch 305/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0559 - mae: 0.6749 Epoch 306/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8302 - mae: 0.6018 Epoch 307/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8969 - mae: 0.6315 Epoch 308/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3418 - mae: 0.6979 Epoch 309/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3480 - mae: 0.7236 Epoch 310/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9808 - mae: 0.7189 Epoch 311/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.4512 - mae: 0.7422 Epoch 312/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9400 - mae: 0.6373 Epoch 313/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7427 - mae: 0.5689 Epoch 314/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9100 - mae: 0.6423 Epoch 315/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9477 - mae: 0.6236 Epoch 316/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9293 - mae: 0.6710 Epoch 317/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0562 - mae: 0.6867 Epoch 318/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8793 - mae: 0.6276 Epoch 319/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8735 - mae: 0.6431 Epoch 320/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9449 - mae: 0.6421 Epoch 321/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1383 - mae: 0.6810 Epoch 322/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7286 - mae: 0.5520 Epoch 323/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1844 - mae: 0.6646 Epoch 324/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8820 - mae: 0.6155 Epoch 325/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0783 - mae: 0.7087 Epoch 326/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9130 - mae: 0.6459 Epoch 327/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0431 - mae: 0.6538 Epoch 328/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9947 - mae: 0.6803 Epoch 329/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9793 - mae: 0.6437 Epoch 330/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8686 - mae: 0.6176 Epoch 331/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8304 - mae: 0.5991 Epoch 332/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0270 - mae: 0.6865 Epoch 333/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0400 - mae: 0.6838 Epoch 334/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3456 - mae: 0.7638 Epoch 335/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9271 - mae: 0.6914 Epoch 336/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1495 - mae: 0.7154 Epoch 337/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.4542 - mae: 0.7570 Epoch 338/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1514 - mae: 0.6707 Epoch 339/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9134 - mae: 0.6118 Epoch 340/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3325 - mae: 0.7508 Epoch 341/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9095 - mae: 0.6332 Epoch 342/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9739 - mae: 0.6055 Epoch 343/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7662 - mae: 0.6054 Epoch 344/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2827 - mae: 0.7390 Epoch 345/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2914 - mae: 0.7563 Epoch 346/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.2493 - mae: 0.7758 Epoch 347/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0907 - mae: 0.7114 Epoch 348/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.3786 - mae: 0.7123 Epoch 349/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8551 - mae: 0.6055 Epoch 350/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1469 - mae: 0.6521 Epoch 351/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7097 - mae: 0.5409 Epoch 352/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8808 - mae: 0.6047 Epoch 353/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9680 - mae: 0.6306 Epoch 354/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7024 - mae: 0.5464 Epoch 355/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9843 - mae: 0.6309 Epoch 356/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7761 - mae: 0.5934 Epoch 357/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9253 - mae: 0.6418 Epoch 358/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.5671 - mae: 0.8066 Epoch 359/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0630 - mae: 0.6956 Epoch 360/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0088 - mae: 0.6227 Epoch 361/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8778 - mae: 0.6120 Epoch 362/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9498 - mae: 0.6263 Epoch 363/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7678 - mae: 0.6044 Epoch 364/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9597 - mae: 0.6340 Epoch 365/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0817 - mae: 0.6192 Epoch 366/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8348 - mae: 0.5877 Epoch 367/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7662 - mae: 0.5881 Epoch 368/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9664 - mae: 0.5928 Epoch 369/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9573 - mae: 0.6290 Epoch 370/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.6304 - mae: 0.5278 Epoch 371/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8430 - mae: 0.5789 Epoch 372/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8640 - mae: 0.5843 Epoch 373/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9841 - mae: 0.6142 Epoch 374/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9159 - mae: 0.6012 Epoch 375/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9002 - mae: 0.5914 Epoch 376/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8551 - mae: 0.6036 Epoch 377/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8661 - mae: 0.6111 Epoch 378/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9698 - mae: 0.6380 Epoch 379/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9427 - mae: 0.6315 Epoch 380/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8365 - mae: 0.5955 Epoch 381/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9933 - mae: 0.6244 Epoch 382/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9553 - mae: 0.6306 Epoch 383/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7316 - mae: 0.5657 Epoch 384/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8707 - mae: 0.6479 Epoch 385/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9535 - mae: 0.6645 Epoch 386/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9664 - mae: 0.6005 Epoch 387/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9679 - mae: 0.6214 Epoch 388/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7908 - mae: 0.5946 Epoch 389/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9552 - mae: 0.6455 Epoch 390/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7900 - mae: 0.5956 Epoch 391/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0715 - mae: 0.6283 Epoch 392/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8858 - mae: 0.6079 Epoch 393/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7583 - mae: 0.5670 Epoch 394/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8169 - mae: 0.5899 Epoch 395/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7155 - mae: 0.5847 Epoch 396/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8196 - mae: 0.5901 Epoch 397/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0240 - mae: 0.6588 Epoch 398/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9131 - mae: 0.6484 Epoch 399/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9884 - mae: 0.6736 Epoch 400/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8741 - mae: 0.5910 Epoch 401/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9638 - mae: 0.6140 Epoch 402/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8647 - mae: 0.5982 Epoch 403/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8738 - mae: 0.6082 Epoch 404/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7383 - mae: 0.5566 Epoch 405/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9866 - mae: 0.6301 Epoch 406/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7982 - mae: 0.5523 Epoch 407/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7243 - mae: 0.5301 Epoch 408/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8406 - mae: 0.5778 Epoch 409/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9663 - mae: 0.5669 Epoch 410/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7309 - mae: 0.5620 Epoch 411/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9836 - mae: 0.5986 Epoch 412/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.6865 - mae: 0.5378 Epoch 413/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8375 - mae: 0.5795 Epoch 414/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8379 - mae: 0.5352 Epoch 415/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8321 - mae: 0.5724 Epoch 416/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.6896 - mae: 0.5526 Epoch 417/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8542 - mae: 0.6395 Epoch 418/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1877 - mae: 0.6802 Epoch 419/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0857 - mae: 0.6420 Epoch 420/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8049 - mae: 0.5627 Epoch 421/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7135 - mae: 0.5287 Epoch 422/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7744 - mae: 0.5833 Epoch 423/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7775 - mae: 0.5693 Epoch 424/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7600 - mae: 0.5784 Epoch 425/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9504 - mae: 0.5906 Epoch 426/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8685 - mae: 0.5775 Epoch 427/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8219 - mae: 0.5566 Epoch 428/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7985 - mae: 0.5378 Epoch 429/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7892 - mae: 0.5556 Epoch 430/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7033 - mae: 0.5366 Epoch 431/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8739 - mae: 0.5788 Epoch 432/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7354 - mae: 0.5097 Epoch 433/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7075 - mae: 0.5305 Epoch 434/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7664 - mae: 0.5638 Epoch 435/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7571 - mae: 0.5417 Epoch 436/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7440 - mae: 0.5285 Epoch 437/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0028 - mae: 0.5797 Epoch 438/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.6854 - mae: 0.5285 Epoch 439/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0163 - mae: 0.6007 Epoch 440/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9600 - mae: 0.5539 Epoch 441/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0031 - mae: 0.6130 Epoch 442/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9660 - mae: 0.5758 Epoch 443/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9638 - mae: 0.5868 Epoch 444/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8775 - mae: 0.5990 Epoch 445/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8684 - mae: 0.5577 Epoch 446/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9240 - mae: 0.5830 Epoch 447/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0143 - mae: 0.6310 Epoch 448/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0097 - mae: 0.6363 Epoch 449/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8631 - mae: 0.6690 Epoch 450/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7302 - mae: 0.5755 Epoch 451/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8937 - mae: 0.6261 Epoch 452/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.1362 - mae: 0.6669 Epoch 453/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7067 - mae: 0.5592 Epoch 454/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7850 - mae: 0.5588 Epoch 455/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.6798 - mae: 0.5574 Epoch 456/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0039 - mae: 0.6136 Epoch 457/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.6463 - mae: 0.5145 Epoch 458/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8742 - mae: 0.5631 Epoch 459/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8664 - mae: 0.5443 Epoch 460/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8504 - mae: 0.5641 Epoch 461/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7152 - mae: 0.5247 Epoch 462/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7752 - mae: 0.5572 Epoch 463/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9584 - mae: 0.6286 Epoch 464/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8388 - mae: 0.6013 Epoch 465/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7425 - mae: 0.5554 Epoch 466/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8407 - mae: 0.5942 Epoch 467/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9818 - mae: 0.6339 Epoch 468/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8249 - mae: 0.5704 Epoch 469/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8735 - mae: 0.5803 Epoch 470/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8964 - mae: 0.6094 Epoch 471/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8151 - mae: 0.5787 Epoch 472/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9678 - mae: 0.6135 Epoch 473/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7901 - mae: 0.5616 Epoch 474/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.6552 - mae: 0.4934 Epoch 475/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7844 - mae: 0.5473 Epoch 476/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7156 - mae: 0.5509 Epoch 477/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8273 - mae: 0.5840 Epoch 478/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9484 - mae: 0.5992 Epoch 479/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8047 - mae: 0.5694 Epoch 480/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7201 - mae: 0.5543 Epoch 481/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7042 - mae: 0.5518 Epoch 482/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8715 - mae: 0.5846 Epoch 483/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8387 - mae: 0.5530 Epoch 484/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.6872 - mae: 0.5267 Epoch 485/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8135 - mae: 0.5773 Epoch 486/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8834 - mae: 0.5813 Epoch 487/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7122 - mae: 0.5464 Epoch 488/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.5971 - mae: 0.4974 Epoch 489/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7398 - mae: 0.5271 Epoch 490/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8097 - mae: 0.5532 Epoch 491/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.5899 - mae: 0.5076 Epoch 492/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7646 - mae: 0.5289 Epoch 493/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.9578 - mae: 0.5835 Epoch 494/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.6186 - mae: 0.5065 Epoch 495/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.6257 - mae: 0.5189 Epoch 496/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8987 - mae: 0.5490 Epoch 497/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.5790 - mae: 0.4977 Epoch 498/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8591 - mae: 0.5642 Epoch 499/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.8338 - mae: 0.5641 Epoch 500/500 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0352 - mae: 0.5944 Model evaluation: Loss (MSE): 0.74, MAE: 0.53
models = {
"Linear Regression": LinearRegression(),
"Lasso": Lasso(),
"K-Neighbors Regressor": KNeighborsRegressor(),
"Decision Tree": DecisionTreeRegressor(),
"Random Forest Regressor": RandomForestRegressor(),
"Gradient Boosting": GradientBoostingRegressor(),
"XGBRegressor": XGBRegressor(),
"CatBoosting Regressor": CatBoostRegressor(verbose=0, iterations = 100),
"AdaBoost Regressor": AdaBoostRegressor(),
"ExtraTreesRegressor": ExtraTreesRegressor(),
"Support Vector Regressor(RBF)": SVR(kernel="rbf"),
"Support Vector Regressor(linear)": SVR(kernel="linear"),
"Nu SVR(rbf)": NuSVR(kernel="rbf"),
"ANN": ANN_model
}
def safe_flatten(y_pred):
"""
Flattens the array if it's a 2D array with shape (n, 1).
Useful for ANN predictions.
"""
if isinstance(y_pred, (np.ndarray, list)) and len(np.shape(y_pred)) == 2 and y_pred.shape[1] == 1:
return y_pred.flatten()
return y_pred
r2_train_score = {}
r2_test_score = {}
def evaluate_model(models, X_train, y_train, X_val, y_val):
for model_name, model in models.items():
model.fit(X_train, y_train)
y_train_pred = model.predict(X_train)
y_test_pred = model.predict(X_val)
y = y_val
y_pred = safe_flatten(y_test_pred)
plt.figure(figsize=(8, 6))
r2 = r2_score(y, y_pred)
sns.scatterplot(x=y, y=y_pred, label='Predicted vs Actual', color='blue')
sns.regplot(x=y, y=y_pred, scatter=False, label='Regression Line', color='red', ci=None)
plt.xlabel('Actual Values')
plt.ylabel('Predicted Values')
plt.title(f'CH4 Actual vs Predicted Values (R² Score: {r2:.4f}) for {model_name} model')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.savefig(f'CH4 Actual vs Predicted Values (R² Score: {r2:.4f}) for {model_name} model.png')
plt.show()
r2_train_score[model_name] = r2_score(y_train, y_train_pred)
r2_test_score[model_name] = r2_score(y_val, y_test_pred)
evaluate_model(models, X_train_scaled, y_train.CH4, X_val_scaled, y_val.CH4)
10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.7597 - mae: 0.5493 10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step 4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step
score = pd.DataFrame(list(zip(models.keys(), r2_train_score.values(), r2_test_score.values())), columns=["Model","r2_train_score", "r2_test_score"])
score
| Model | r2_train_score | r2_test_score | |
|---|---|---|---|
| 0 | Linear Regression | 0.257236 | 0.273732 |
| 1 | Lasso | 0.000000 | -0.033079 |
| 2 | K-Neighbors Regressor | 0.746015 | 0.721042 |
| 3 | Decision Tree | 0.995971 | 0.452798 |
| 4 | Random Forest Regressor | 0.950031 | 0.796166 |
| 5 | Gradient Boosting | 0.915502 | 0.760806 |
| 6 | XGBRegressor | 0.995611 | 0.790316 |
| 7 | CatBoosting Regressor | 0.959128 | 0.821011 |
| 8 | AdaBoost Regressor | 0.708777 | 0.549022 |
| 9 | ExtraTreesRegressor | 0.995971 | 0.832564 |
| 10 | Support Vector Regressor(RBF) | 0.518792 | 0.533648 |
| 11 | Support Vector Regressor(linear) | 0.213773 | 0.195725 |
| 12 | Nu SVR(rbf) | 0.491429 | 0.525306 |
| 13 | ANN | 0.938276 | 0.762182 |
# Set positions
x = np.arange(len(score['Model']))
width = 0.35 # Width of the bars
# Create plot
fig, ax = plt.subplots(figsize=(16, 8))
bars1 = ax.bar(x - width/2, score['r2_train_score'], width, label='Train R²', color='skyblue')
bars2 = ax.bar(x + width/2, score['r2_test_score'], width, label='Validation R²', color='salmon')
# Add labels and title
ax.set_xlabel('ML Models')
ax.set_ylabel('R² Score')
ax.set_title('CH4 Train vs Validation R² Score for Different Models')
ax.set_xticks(x)
ax.set_xticklabels(score['Model'], rotation=45)
ax.legend()
# Add R2 score text on top of bars
for bar in bars1 + bars2:
yval = bar.get_height()
ax.text(bar.get_x() + bar.get_width()/2.0, yval + 0.01, f'{yval:.2f}', ha='center', va='bottom')
plt.tight_layout()
plt.savefig("CH4 Train vs Validation R² Score for Different Models")
plt.show()
import zipfile
import os
with zipfile.ZipFile('kaggle_output.zip', 'w') as zipf:
for file in os.listdir():
if file.endswith(('.png')):
zipf.write(file)